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KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 86485 Last moment tuitions
Last Minute Tutorials | KDD | Knowledge Discovery of Data
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 17643 Last Minute Tutorials
How Data Minng works or The KDD Process
 
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This video explains about the process of knowledge discovery in databases.
Views: 13593 kalyani chandra
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
[Коллоквиум]: Rough sets: A tool for qualitative knowledge discovery
 
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Rough set theory (RST) was introduced in the early 1980s by Z. Pawlak (1982) and has become a well researched tool for knowledge discovery. The basic assumption of RST is that information is presented and perceived up to a certain granularity: "The information about a decision is usually vague because of uncertainty and imprecision coming from many sources [. . . ] Vagueness may be caused by granularity of representation of the information. Granularity may introduce an ambiguity to explanation or prescription based on vague information" (Pawlak and Słowin ́ski, 1993). In contrast to other machine learning or statistical methods, the original rough set approach uses only the information presented by the data itself and does not rely on outside distributional or other parameters. RST relies only on the principle of indifference and the nominal scale assumption. It has been applied in many fields, most recently in the investigation of complex adaptive systems, interactive granular computing, and big data analysis (Skowron et al., 2016). In my talk I will present the basic concepts of RST as well as non–parametric methods for feature reduction, data filtering, significance testing and model selection.
Views: 2912 ФКН ВШЭ
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1712 Audiopedia
What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning
 
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What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning - KNOWLEDGE DISCOVERY definition - KNOWLEDGE DISCOVERY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. nowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It is often described as deriving knowledge from the input data. Knowledge discovery developed out of the data mining domain, and is closely related to it both in terms of methodology and terminology. The most well-known branch of data mining is knowledge discovery, also known as knowledge discovery in databases (KDD). Just as many other forms of knowledge discovery it creates abstractions of the input data. The knowledge obtained through the process may become additional data that can be used for further usage and discovery. Often the outcomes from knowledge discovery are not actionable, actionable knowledge discovery, also known as domain driven data mining, aims to discover and deliver actionable knowledge and insights. Another promising application of knowledge discovery is in the area of software modernization, weakness discovery and compliance which involves understanding existing software artifacts. This process is related to a concept of reverse engineering. Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An entity relationship is a frequent format of representing knowledge obtained from existing software. Object Management Group (OMG) developed specification Knowledge Discovery Metamodel (KDM) which defines an ontology for the software assets and their relationships for the purpose of performing knowledge discovery of existing code. Knowledge discovery from existing software systems, also known as software mining is closely related to data mining, since existing software artifacts contain enormous value for risk management and business value, key for the evaluation and evolution of software systems. Instead of mining individual data sets, software mining focuses on metadata, such as process flows (e.g. data flows, control flows, & call maps), architecture, database schemas, and business rules/terms/process.
Views: 2267 The Audiopedia
Knowledge Discovery and Datamining | University of East Anglia (UEA)
 
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The School of Computing Sciences is one of the largest and most experienced computing schools in the UK. We offer excellent teaching, research, facilities and exciting course modules, creating a dynamic programme targeted at one of the most rapidly growing sectors of the job market. Our research is highly acclaimed, with 95% of our work rated as world-leading, internationally excellent or recognised in the most recent Research Assessment Exercise (RAE 2008). http://www.uea.ac.uk/cmp
Views: 1837 UEA
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 237771 Thales Sehn Körting
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
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Supervised and unsupervised learning algorithms
Views: 71059 Nathan Kutz
Data Science for Business: Data Mining Process and CRISP DM
 
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This lesson provides an introduction to the data mining process with a focus on CRISP-DM. This video was created by Cognitir (formerly Import Classes). Cognitir is a global company that provides live training courses to business & finance professionals globally to help them acquire in-demand tech skills. For additional free resources and information about training courses, please visit: www.cognitir.com
Views: 16056 Cognitir
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 135057 nptelhrd
Apriori Algorithm Video, KDD Knowledge Discovery in Database
 
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This is a video demonstration of finding representative rules and sets using the Apriori algorithm.
Views: 32691 Laurel Powell
Kdd process Knowledge Discovery In Databases(KDD)
 
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KDD process,database management,software engineering,data mining,knowledge process,artificial intelligence,machine learning
Views: 171 Edu World
Machine Learning Knowledge Extraction MAKE it short
 
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MAKE stands for MAchine Learning & Knowledge Extraction. Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and statistical learning. The inverse probability allows to infer unknowns, learn from data and make predictions to support decision making. Whether in social networks, recommender systems, smart health or smart factory applications, the increasingly complex data sets require efficient, useful and useable intelligence for knowledge discovery and knowledge extraction. A synergistic combination of methodologies and approaches of two domains offer ideal conditions towards unraveling these challenges and to foster new, efficient and user-friendly machine learning algorithms and knowledge extraction tools: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), aiming at augmenting human intelligence with computational intelligence and vice versa. Successful Machine Learning & Knowledge extraction needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from 7 fields: in short: 1-data, 2-learning, 3-graphs, 4-topology, 5-entropy, 6-visualization, and 7-privacy; see http://hci-kdd.org/about-the-holzinger-group https://cd-make.net/about/ http://www.mdpi.com/journal/make/about Andreas Holzinger, 14.05.2017
Views: 1198 Andreas Holzinger
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
What is Data Mining?
 
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I Have No Intention To Claim The Ownership Of This Video All Credits To The Owner Of This Video! This Has Been Upload For Educational Purpose Only. Please Do Not Take Down This Channel! If You Do Not Agree Please Message Me So That I Can Delete The Video! Thank You Very Much! Original Video Link: https://www.youtube.com/watch?v=R-sGvh6tI04 Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] It is an interdisciplinary subfield of computer science.[1][2][3] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4]The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[5] It also is a buzzword[6] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[7] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[8] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations. Lets Connect: Twitter: https://twitter.com/BLAmedia1 Google+: https://plus.google.com/115816603020714793797 Facebook: https://www.facebook.com/BLAmedia-1884144591836064 LinkedIn: https://www.linkedin.com/in/blamedia
Views: 21 Pedro Puerto
Knowledge discovery and data mining in pharmaceutical cancer research (KDD 2011)
 
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Knowledge discovery and data mining in pharmaceutical cancer research KDD 2011 Paul Rejto Biased and unbiased approaches to develop predictive biomarkers of response to drug treatment will be introduced and their utility demonstrated for cell cycle inhibitors. Opportunities to leverage the growing knowledge of tumors characterized by modern methods to measure DNA and RNA will be shown, including the use of appropriate preclinical models and selection of patients. Furthermore, techniques to identify mechanisms of resistance prior to clinical treatment will be discussed. Prospects for systematic data mining and current barriers to the application of precision medicine in cancer will be reviewed along with potential solutions.
Data mining and knowledge discovery, and how to discover patterns and relationships
 
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Data mining and as it sometimes called “knowledge discovery” is the process of analyzing large data and discovering patterns and relationships from different perspectives and summarizing it into useful information. Prof. Othman Ibrahim Al-Salloum education channel on YouTube, topics are: management information systems, e-learning, scientific research, quality management, project management. https://www.youtube.com/user/TubeRiyadh/ قناة أ.د. عثمان بن ابراهيم السلوم التعليمية على اليوتيوب ، المواضيع: نظم المعلومات الادارية ، التعليم الالكتروني ، البحث العلمي، ادارة الجودة ، ادارة المشاريع. https://www.youtube.com/user/TubeRiyadh/
Views: 1345 MIS
Information Visualization for Knowledge Discovery
 
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Information Visualization for Knowledge Discovery Ben Shneiderman [University of Maryland--College Park] Abstract: Interactive information visualization tools provide researchers with remarkable capabilities to support discovery. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional data. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100msec updates even for million-record databases. This talk will start by reviewing the growing commercial success stories such as www.spotfire.com, www.smartmoney.com/marketmap and www.hivegroup.com. Then it will cover recent research progress for visual exploration of large time series data applied to financial, medical, and genomic data (www.cs.umd.edu/hcil/timesearcher ). These strategies of unifying statistics with visualization are applied to electronic health records (www.cs.umd.edu/hcil/lifelines2) and social network data (www.cs.umd.edu/hcil/socialaction and www.codeplex.com/nodexl). Demonstrations will be shown. BEN SHNEIDERMAN is a Professor in the Department of Computer Science and Founding Director (1983-2000) of the Human-Computer Interaction Laboratory at the University of Maryland. He was elected as a Fellow of the Association for Computing (ACM) in 1997 and a Fellow of the American Association for the Advancement of Science (AAAS) in 2001. He received the ACM SIGCHI Lifetime Achievement Award in 2001. Ben is the author of "Designing the User Interface: Strategies for Effective Human-Computer Interaction" (5th ed. March 2009, forthcoming) http://www.awl.com/DTUI/. With S. Card and J. Mackinlay, he co-authored "Readings in Information Visualization: Using Vision to Think" (1999). With Ben Bederson he co-authored The Craft of Information Visualization (2003). His book Leonardos Laptop appeared in October 2002 (MIT Press) (http://mitpress.mit.edu/leonardoslaptop) and won the IEEE book award for Distinguished Literary Contribution.
Views: 23850 CITRIS
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 257345 Last moment tuitions
How data mining works
 
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Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 631 Technology mart
1-Association Rules
 
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Association rule learning is a method for discovering interesting relations between variables in large databases.
Views: 1661 Mena A.A
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 286330 Last moment tuitions
"Data Science":What Is Data Mining | Types of Data Mining | Data Science(2019) -ExcelR
 
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#Datamining #Dataminingmethods #datamining(2019) ExcelR : Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Random forest algorithm is a supervised classification algorithm. As the name suggest, this algorithm creates the forest with a number of trees. Things we will learn in this video: 1)What is Data Mining? 2)Types of Data Mining 3)What is supervised learning? 4)What is Unsupervised Learning? 5)Process for Data mining To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To enroll for the virtual online course click here https://goo.gl/m4MYd8 To register for classroom training click here https://goo.gl/UyU2ve SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx Introduction To Data mining using R click here https://goo.gl/muRASy For Peer-To-Peer Network Analysis click here https://goo.gl/HcAjqu ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
What is Data Mining
 
08:10
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery[citation needed], commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java"(which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
Views: 52531 John Paul
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
01:13:27
( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 107149 edureka!
Association Rules شرح
 
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Association Rules شرح - Data Mining
Views: 42367 Emad Tolba
HCDF: A Hybrid Community Discovery Framework
 
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Google Tech Talk March 11, 2010 ABSTRACT Presented by Tina Eliassi-Rad. We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework ), can effectively incorporate hints from a number of other community detection algorithms and produce results that outperform the constituent parts. We describe two HCDF-based approaches which are: (1) effective, in terms of link prediction performance and robustness to small perturbations in network structure; (2) consistent, in terms of effectiveness across various application domains; (3) scalable to very large graphs; and (4) nonparametric. Our extensive evaluation on a collection of diverse and large real-world graphs, with millions of links, show that our HCDF-based approaches (a) achieve up to 0.22 improvement in link prediction performance as measured by area under ROC curve (AUC), (b) never have an AUC that drops below 0.91 in the worst case, and (c) find communities that are robust to small perturbations of the network structure as defined by Variation of Information (an entropy-based distance metric). Dr. Tina Eliassi-Rad, Lawrence Livermore National Laboratory http://people.llnl.gov/eliassirad1 Tina Eliassi-Rad (http://eliassi.org) is a computer scientist and principal investigator at the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. She will join the faculty at the Department of Computer Science at Rutgers University in Fall 2010. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research interests include data mining, machine learning, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, and complex networks. She serves as an action editor for the Data Mining and Knowledge Discovery Journal.
Views: 4373 GoogleTechTalks
Time Series data Mining Using the Matrix Profile part 1
 
01:14:15
Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 2887 KDD2017 video
Data mining technique
 
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Views: 2268 IMSUC FLIP
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
 
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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 65630 StudyKorner
Geoff Webb - Analysis and Mining Large Data Sets
 
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Geoffrey I. Webb is Professor of Computer Science at Monash University, Founder and Director of Data Mining software development and consultancy company G. I. Webb and Associates, and Editor-in-Chief of the journal Data Mining and Knowledge Discovery. Before joining Monash University he was on the faculty at Griffith University from 1986 to 1988 and then at Deakin University from 1988 to 2002. Webb has published more than 180 scientific papers in the fields of machine learning, data science, data mining, data analytics, big data and user modeling. He is an editor of the Encyclopedia of Machine Learning. Webb created the Averaged One-Dependence Estimators machine learning algorithm and its generalization Averaged N-Dependence Estimators and has worked extensively on statistically sound association rule learning. Webb's awards include IEEE Fellow, the IEEE International Conference on Data Mining Outstanding Service Award, an Australian Research Council Outstanding Researcher Award and multiple Australian Research Council Discovery Grants. Webb is a Foundation Member of the Editorial Advisory Board of the journal Statistical Analysis and Data Mining, Wiley Inter Science. He has served on the Editorial Boards of the journals Machine Learning, ACM Transactions on Knowledge Discovery in Data,User Modeling and User Adapted Interaction,and Knowledge and Information Systems. https://en.wikipedia.org/wiki/Geoff_Webb http://www.infotech.monash.edu.au/research/profiles/profile.html?sid=4540&pid=122 http://www.csse.monash.edu.au/~webb Interviewed by Kevin Korb and Adam Ford Many thanks for watching! - Support me via Patreon: https://www.patreon.com/scifuture - Please Subscribe to this Channel: http://youtube.com/subscription_center?add_user=TheRationalFuture - Science, Technology & the Future website: http://scifuture.org
Seminar on Neural Network - Datamining
 
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Presented by Karthik A
Views: 1156 Karthik Gowda
2018 HOLZINGER Machine Learning Research Topics
 
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Andreas Holzinger promotes a synergistic approach by integration of two areas to understand intelligence to realize context-adaptive systems: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD). Andreas has pioneered in interactive machine learning (iML) with the human-in-the-loop. Andreas Holzingers’ goal is to augment human intelligence with artificial intelligence to help to solve problems in health informatics. Due to raising legal and privacy issues in the European Union glass box AI approaches will become important in the future to be able to make decisions transparent, re-traceable, thus understandable. Andreas Holzingers’ aim is to explain why a machine decision has been made, paving the way towards explainable AI. 00:34 [1] June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo & Namkug Kim 2017. Deep learning in medical imaging: general overview. Korean journal of radiology, 18, (4), 570-584, doi:10.3348/kjr.2017.18.4.570. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5447633/ 01:26 [2] Andreas Holzinger 2017. Introduction to Machine Learning and Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1, (1), 1-20, doi:10.3390/make1010001. https://www.mdpi.com/2504-4990/1/1/1 03:21 [3] Andreas Holzinger 2013. Human–Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, Alfredo, Kittl, Christian, Simos, Dimitris E., Weippl, Edgar & Xu, Lida (eds.) Multidisciplinary Research and Practice for Information Systems, Springer Lecture Notes in Computer Science LNCS 8127. Heidelberg, Berlin, New York: Springer, pp. 319-328, doi:10.1007/978-3-642-40511-2_22. https://link.springer.com/chapter/10.1007/978-3-642-40511-2_22 04:00 [4] Andreas Holzinger & Klaus-Martin Simonic (eds.) 2011. Information Quality in e-Health. Lecture Notes in Computer Science LNCS 7058, Heidelberg, Berlin, New York: Springer, doi:10.1007/978-3-642-25364-5. https://www.springer.com/de/book/9783642253638 04:26 [5] Andreas Holzinger, Matthias Dehmer & Igor Jurisica 2014. Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions. Springer/Nature BMC Bioinformatics, 15, (S6), I1, doi:10.1186/1471-2105-15-S6-I1. https://www.ncbi.nlm.nih.gov/pubmed/25078282 04:40 [6] Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams & Nando De Freitas 2016. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104, (1), 148-175, doi:10.1109/JPROC.2015.2494218. https://www.semanticscholar.org/paper/Taking-the-Human-Out-of-the-Loop%3A-A-Review-of-Shahriari-Swersky/5ba6dcdbf846abb56bf9c8a060d98875ae70dbc8 05:10 [7a] Quoc V. Le, Marc'aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean & Andrew Y. Ng 2011. Building high-level features using large scale unsupervised learning. arXiv:1112.6209.05:16 https://arxiv.org/abs/1112.6209 [7b] Quoc V. Le. Building high-level features using large scale unsupervised learning. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013. IEEE, 8595-8598, doi:10.1109/ICASSP.2013.6639343. https://ieeexplore.ieee.org/abstract/document/6639343 05:24 [8] Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, (7639), 115-118, doi:10.1038/nature21056. https://cs.stanford.edu/people/esteva/nature [9] Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In: Pereira, Fernando, Burges, Christopher .J.C., Bottou, Leon & Weinberger, Kilian Q., eds. Advances in neural information processing systems (NIPS 2012), 2012 Lake Tahoe. NIPS, 1097-1105. https://github.com/abhshkdz/papers/blob/master/reviews/imagenet-classification-with-deep-convolutional-neural-networks.md 06:15 [10] Randy Goebel, Ajay Chander, Katharina Holzinger, Freddy Lecue, Zeynep Akata, Simone Stumpf, Peter Kieseberg & Andreas Holzinger. Explainable AI: the new 42? Springer Lecture Notes in Computer Science LNCS 11015, 2018 Cham. Springer, 295-303, doi:10.1007/978-3-319-99740-7_21. https://link.springer.com/chapter/10.1007/978-3-319-99740-7_21 06:45 [11] Zhangzhang Si & Song-Chun Zhu 2013. Learning and-or templates for object recognition and detection. IEEE transactions on pattern analysis and machine intelligence, 35, (9), 2189-2205, doi:10.1109/TPAMI.2013.35. https://ieeexplore.ieee.org/document/6425379 About the concept of the human-in-the-loop: [1] Andreas Holzinger 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131, doi:10.1007/s40708-016-0042-6. https://link.springer.com/article/10.1007/s40708-016-0042-6 https://hci-kdd.org http://www.aholzinger.at
Views: 808 Andreas Holzinger
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 162755 Well Academy
K-Nearest Neighbor Classification ll KNN Classification Explained with Solved Example in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 25028 5 Minutes Engineering
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial
 
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Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Hey guys and welcome to another fun and easy machine tutorial on Eclat. Today we are going to be analyzing what video games get sold more frequently using an associated rule algorithm called Eclat. The Eclat algorithm which is an acronym for Equivalence CLAss Transformation is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys Halo, he also buys Gears of War. This type of pattern is called association rules and is used in many application domains such as recommender systems. In the previous lecture we discussed the Apriori Algorithm. Eclat is one of the algorithms which is meant to improve the Efficiency of Apriori. Eclat is a depth-first search algorithm using set intersection. It is a naturally elegant algorithm suitable for both sequential as well as parallel execution with locality-enhancing properties. It was first introduced by Zaki, Parthasarathy, Li and Ogihara in a series of papers written in 1997. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 6714 Augmented Startups
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Cost-sensitive classification vs. cost-sensitive learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8606 WekaMOOC
Mod-01 Lec-04 Clustering vs. Classification
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 21804 nptelhrd
5 must read machine learning books | Read in order
 
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These 5 books are highly recommended for any budding Machine Learning engineer. 1. Practical Machine Learning by Sebastian Raschka https://www.amazon.in/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_fkmrnull_1?keywords=practical+machine+learning+sebastian&qid=1557683932&s=gateway&sr=8-1-fkmrnull 2. Data Science from Scratch - Joel Grus https://www.amazon.in/Data-Science-Scratch-Joel-Grus/dp/9352130960/ref=sr_1_3?crid=VFQ2TMK4KMGD&keywords=data+science+from+scratch&qid=1557684049&s=gateway&sprefix=data+science+from+%2Caps%2C254&sr=8-3 3. Marketing Data Science - Thomas Miller https://www.amazon.in/Marketing-Data-Science-Techniques-Predictive-ebook/dp/B00XANZZ4A/ref=sr_1_fkmr0_1?keywords=marketing+machine+learning+miller&qid=1557684108&s=gateway&sr=8-1-fkmr0 4. Advances in Machine Learning and Data Mining for Astronomy - Way, Scargle, Srivastava and Ali https://www.amazon.in/Advances-Learning-Astronomy-Knowledge-Discovery/dp/1138199303/ref=sr_1_fkmrnull_1?keywords=advances+in+machine+learning+and+data+mining+in+astronomy+way&qid=1557684167&s=gateway&sr=8-1-fkmrnull 5. Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall/CRC The R Series) https://www.amazon.in/Data-Science-Approach-Computational-Reasoning/dp/1482234815/ref=sr_1_4?keywords=Data+science+in+R+chapman&qid=1557684270&s=gateway&sr=8-4 Another interesting book on Indian IT employee *Walking Over Employees' Shoulder by Humminghum Spinfold Walking Over Employees Shoulder (MBBD2019 Book 1) https://www.amazon.in/dp/B07R81CQ3P/ref=cm_sw_r_other_apa_i_9CDYCbKSFSAJ4
Views: 457 Mirror Neuron
"Artificial Intelligence with Bayesian Networks" with Dr. Lionel Jouffe
 
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Title: Artificial Intelligence with Bayesian Networks - Data Mining, Knowledge Modeling and Causal Analysis Speaker: Dr. Lionel Jouffe Date: 1/12/2018 Abstract: Probabilistic models based on directed acyclic graphs have a long and rich tradition, beginning with the work of geneticist Sewall Wright in the 1920s. Variants have appeared in many fields. Within Statistics, such models are known as directed graphical models; within Cognitive Science and Artificial Intelligence, such models are known as Bayesian Networks (BNs), a term coined in 1985 by UCLA Professor Judea Pearl to honor the Rev. Thomas Bayes (1702-1761), whose rule for updating probabilities in the light of new evidence is the foundation of the approach. BNs provide an elegant and sound approach to represent uncertainty and to carry out rigorous probabilistic inference by propagating the pieces of evidence gathered on a subset of variables on the remaining variables. BNs are not only effective for representing expert’s belief, uncertain knowledge and vague linguistic representations of knowledge via an intuitive graphical representation, but are also a powerful Knowledge Discovery tool when associated with Machine Learning/Data Mining techniques. In 2004, the MIT Press of Technology (Massachusetts Institute of Technology) classified Bayesian Machine Learning at the 4th rank among the “10 Emerging Technologies That Will Change Your World”. Most recently, Judea Pearl, the father of BNs, received the 2012 ACM A.M. Turing Award, the most prestigious award in Computer Science, widely considered the "Nobel Prize in Computer Science," for contributions that transformed Artificial Intelligence, especially for the development of the theoretical foundations for reasoning under uncertainty using BNs. Over the last 25 years, BNs have then emerged as a practically feasible form of knowledge representation and as a new comprehensive data analysis framework. With the ever-increasing computing power, their computational efficiency and inherently visual structure make them attractive for exploring and explaining complex problems. BNs are now a powerful tool for deep understanding of very complex and high-dimensional problem domains. Deep understanding means knowing, not merely how things behaved yesterday, but also how things will behave under new hypothetical circumstances tomorrow. More specifically, a BN allows explicit reasoning, and deliberate reasoning to allow the anticipation of the consequences of actions that have not yet been taken. We will use this 45-minute tutorial for describing what BN are, how we can design these probabilistic expert systems by expertise and how we can use data to automatically machine-learn these models. SPEAKER Dr. Lionel Jouffe, Co-founder and CEO of Bayesia S.A.S. Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has been working in the field of Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development BayesiaLab, which has since emerged as a leading software package for knowledge discovery, data mining and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities as well as in business and industry. MODERATOR Plamen Petrov, Director of Cognitive Technology, KPMG LLP; SIGAI Industry Liaison Officer MODERATOR Rose Paradis, Data Scientist at Leidos Health and Life Sciences; SIGAI Secretary/Treasurer
Data Science in 30 Minutes: A Conversation with Gregory Piatetsky-Shapiro, President of KDnuggets.
 
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KDnuggets' Gregory Piatetsky-Shapiro, Ph.D  joined The Data Incubator on January 11th for the first 2018 installment of our free online webinar series, Data Science in 30 minutes! Gregory discussed his career - from Data Mining to Data Science and examine current trends in the field. https://www.kdnuggets.com https://www.thedataincubator.com From Data Mining to Knowledge Discovery to Data Science: Gregory Piatetsky talked about his pioneering career in data science, including founding KDnuggets, and co-founding KDD Conferences and ACM SIGKDD, and examined current trends in the field, Data Science Automation, citizen Data Scientists, and implications of AI. About the speakers: Gregory Piatetsky-Shapiro, Ph.D., is a well-known Data Scientist, and the President of KDnuggets, a leading site for Analytics, Big Data, Data Science, Data Mining, and Machine Learning. Gregory is a co-founder of KDD (Knowledge Discovery and Data Mining, the top research conference in the field) and a co-founder and past chair of ACM SIGKDD, the professional association for Data Mining and Data Science. See also http://www.kdnuggets.com/gps.html or Gregory's Wikipedia page. Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners. Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves. Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.
Views: 1141 The Data Incubator
Dealing With Noisy Data : Binning Technique [Data Mining] (HINDI)
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 16193 5 Minutes Engineering
Efficient Similar Region Search with Deep Metric Learning
 
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Authors: Yiding Liu (Nanyang Technological University); Kaiqi Zhao (Nanyang Technological University); Gao Cong (Nanyang Technological University) More on http://www.kdd.org/kdd2018/
Views: 194 KDD2018 video