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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: 72144 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: 16175 Last Minute Tutorials
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: 229751 Thales Sehn Körting
How Data Minng works or The KDD Process
 
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This video explains about the process of knowledge discovery in databases.
Views: 12654 kalyani chandra
[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: 2725 ФКН ВШЭ
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: 31593 Laurel Powell
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.
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: 1804 UEA
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 technique
 
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Views: 985 IMSUC FLIP
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: 14841 Cognitir
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.
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: 226979 Last moment tuitions
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: 23731 CITRIS
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning ►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 Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ 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: 58453 Augmented Startups
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: 2126 The Audiopedia
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: 53617 StudyKorner
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: 175 KDD2018 video
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: 134568 nptelhrd
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: 522 Technology mart
What are the current challenges in Knowledge Discovery? - Ron Daniel
 
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Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 51 OpenMinTeD
Data, Knowledge and Discovery: Machine Learning meets Natural Science
 
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Author: Hugh Durrant-Whyte Abstract: Increasingly it is data, vast amounts of data, that drives scientific discovery. At the heart of this so-called ""fourth paradigm of science"" is the rapid development of large scale statistical data fusion and machine learning methods. While these developments in ""big data"" methods are largely driven by commercial applications such as internet search or customer modelling, the opportunity for applying these to scientific discovery is huge. This talk will describe a number of applied machine learning projects addressing real-world inference problems in physical, life and social science areas. In particular, I will describe a major Science and Industry Endowment Fund (SIEF) project, in collaboration with the NICTA and Macquarie University, looking to apply machine learning techniques to discovery in the natural sciences. This talk will look at the key methods in machine learning that are being applied to the discovery process, especially in areas like geology, ecology and biological discovery. ACM DL: http://dl.acm.org/citation.cfm?id=2785467 DOI: http://dx.doi.org/10.1145/2783258.2785467
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 37242 Well Academy
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
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( 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: 104961 edureka!
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 70292 edureka!
Text-mining for rapid knowledge discovery
 
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Discussion as to how Elsevier Life Sciences Technologies help researchers gain access to insights and scientific data details without having to read dense, detailed articles in detail.
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
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Supervised and unsupervised learning algorithms
Views: 67181 Nathan Kutz
DOE CSGF 2012: Discovering Knowledge from Massive Networks and Science Data
 
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View more information on the DOE CSGF Program at http://www.krellinst.org/csgf Alok Choudhary John G. Searle Professor of Electrical Engineering and Computer Science, Northwestern University Knowledge discovery in science and engineering has been driven by theory, experiments and more recently by large-scale simulations suing high-performance computers. Modern experiments and simulations involving satellites, telescopes, high-throughput instruments, imaging devices, sensor networks, accelerators, and supercomputers yield massive amounts of data. At the same time, the world, including social communities is creating massive amounts of data at an astonishing pace. Just consider Facebook, Google, Articles, Papers, Images, Videos and others. But, even more complex is the network that connects the creators of data. There is knowledge to be discovered in both. This represents a significant and interesting challenge for HPC and opens opportunities for accelerating knowledge discovery. In this talk, followed by an introduction to high-end data mining and the basic knowledge discovery paradigm, we present the process, challenges and potential for this approach. We will present many case examples, results and future directions including (1) Discovering knowledge from massive datasets from science applications including climate and medicine; (2) Real-time stream mining of text from millions of and tweets to identify influencers and sentiments of people; (3) Discovering knowledge from massive social networks containing millions of nodes and hundreds of billions of edges from real world Facebook, twitter and other social network data and (4) predicting structures from Simulation data. The talk will be illustrative and example driven and may include 1-2 live demonstrations.
Views: 123 Krell Institute
The Live Wire - Knowledge Discovery in Databases
 
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Dr. Pamela Thompson, Adjunct faculty member, and Lavanya Loganarayanan, recent graduate, were the guests on the August 20 edition of “The Live Wire,” Inside UNC Charlotte’s streaming webcast. They discussed the course “Knowledge Discovery in Databases”, which is part of UNC Charlotte’s Data Science Initiative, and how UNC Charlotte students have analyzed diverse data sets related to sharks and have discovered that certain patterns emerge.
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: 19 Pedro Puerto
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: 134625 Well Academy
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
Apriori Algorithm
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 16335 Anuradha Bhatia
The ART of Data Mining – Practical learnings from real-world data mining applications
 
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Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in: (1) The art of understanding the data better - (e.g. visualization of text data in a semantic space) (2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features) (3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels) (4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning) (5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer) (6) The art of using textual features with structured features to build models, etc. The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.
Views: 1908 HasGeek TV
Data Mining Using R: Introduction to Data Mining Techniques | Machine Learning - ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to various Data mining unsupervised techniques namely Clustering, Dimension Reduction, Association Rules, Recommender System or Collaborative filtering, Network Analytics. Things you will learn in this video 1)What is DataMining 2)DataMining in Nutshell 3)Types of methods 4)DataMining process 5)Approaches 6)Types of Clustering Algorithms To buy eLearning course on DataScience 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 For Introduction to Clustering Analysis clicks here https://goo.gl/wuXN48 For Introduction to K-mean clustering click here https://goo.gl/PYqXRJ #ExcelRSolutions #DataMining#clusteringTechniques #datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 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
Association Rules شرح
 
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Association Rules شرح - Data Mining
Views: 40816 Emad Tolba
Dimensionalities of Knowledge Discovery using Data, Text and Web Mining (Prof. Richard Segall)
 
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Plenary Session delivered at IIIS Conference on July 2012
Views: 361 IIISchannel
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 181687 Well Academy
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: 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
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: 1307 MIS
Lifelong Machine Learning and Computer Reading the Web (KDD 2016)
 
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Lifelong Machine Learning and Computer Reading the Web KDD 2016 Zhiyuan Chen Estevam R. Hruschka, Jr. Bing Liu This tutorial introduces Lifelong Machine Learning (LML) and Machine Reading. The core idea of LML is to learn continuously and accumulate the learned knowledge, and to use the knowledge to help future learning, which is perhaps the hallmark of human learning and human intelligence. By us- ing prior knowledge seamlessly and effortlessly, we humans can learn without a lot of training data, but current machine learning algorithms tend to need a huge amount of training data. LML aims to mimic this human capability. Machine Reading is a research area with the goal of building systems to read natural language text. Among different approaches employed in Machine Reading, this tutorial focuses on projects and approaches that use the idea of LML. Most current machine learning (ML) algorithms learn in isolation. They are designed to address a specific problem using a single dataset. That is, given a dataset, an ML algorithm is executed on the dataset to build a model. Although this type of isolated learning is very useful, it does not have the ability to accumulate past knowledge and to make use of the knowledge for future learning, which we believe are critical for the future of machine learning and data mining. LML aims to design and develop computational systems and algorithms with this capability, i.e., to learn as humans do in a lifelong manner. In this tutorial, we introduce this important problem and the existing LML techniques and discuss opportunities and challenges of big data for lifelong machine learning. We also want to motivate researchers and practitioners to actively explore LML as the big data provides us a golden opportunity to learn a large volume of diverse knowledge, to connect different pieces of it, and to use it to raise data mining and machine learning to a new level.