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RapidMiner Stats (Part 1): Basics and Loading Data
 
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This is the beginning of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. This video briefly describes a data set to be used in the entire segment and shows how to read in a file in a CSV format and how to convert it into a RapidMiner data store. As this is the first video in the series, it also introduces some fundamental concepts of RapidMiner and the way you create analytic processes, manipulate operators and their parameters, open design and results views, and inspect the generated results. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 812 ironfrown
RapidMiner Stats (Part 2): Simple Data Exploration
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to use RapidMiner "Statistics" tab to explore attributes of a loaded data set. It briefly explains different attribute types, such as numeric, polynomial and binomial, and then shows how to create 2D and 3D scatter plots of numeric attributes. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1651 ironfrown
RapidMiner Stats (Part 3): Working with Attributes
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to manipulate attributes to select them, to create new and modify existing attributes, and how to discretize values of a continuous (real) attribute into a nominal (categorical) attribute. A simple pie chart is then used to visualize the resulting data. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1150 ironfrown
RapidMiner Stats (Part 4): Working with Aggregates
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to use an aggregate operator to derive various statistics, such as mean, median, mode or standard deviation from a data sample, for both numerical and nominal attributes. It is explained how to group aggregates by a nominal attribute and thus produce the relevant statistics for each of the nominal attribute levels (possible values). Most importantly, the aggregate operator return all statistics in the form of data examples, which means they can be used by other operators as input to further processing. As several aggregates are produced in the course of this video, it is also shown how to create many copies of the same data set using a multiply operator. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1163 ironfrown
RapidMiner Stats (Part 6): Histograms
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video explains how to use histograms to analyze distribution of attribute values, how to overlay and compare two different attributes or their categories based on their distribution, and how to utilize density (distribution) curves to simplify the visual representation of normally distributed attributes. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 740 ironfrown
RapidMiner Stats (Part 7): Cumulative Frequency Distribution
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video shows how to use advanced charts to create statistical plots that are not available in RapidMiner standard suite of charts. This process is illustrated by developing a cumulative frequency distribution chart. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 364 ironfrown
How to Analyze Text Stats
 
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Text Statistics Analyzer is a useful utility for generating quick stats of any text. Char, word and line statistics are available. You can also export the data into CSV file for further analysis. Download the software on https://vovsoft.com/software/text-statistics-analyzer/
Views: 611 Vovsoft
The Library as Dataset: Text Mining at Million-Book Scale
 
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What do you do with a library? The large-scale digital collections scanned by Google and the Internet Archive have opened new ways to interact with books. The scale of digitization, however, also presents a challenge. We must find methods that are powerful enough to model the complexity of culture, but simple enough to scale to millions of books. In this talk I'll discuss one method, statistical topic modeling. I'll begin with an overview of the method. I will then demonstrate how to use such a model to measure changes over time and distinctions between sub-corpora. Finally, I will describe hypothesis tests that help us to distinguish consistent patterns from random variations. David Mimno is a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.
Views: 2327 YaleUniversity
Eliminate writer's block with the Ultimate Research Assistant
 
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Visit us at http://ultimate-research-assistant.com/ This video shows high school and college students and other researchers how to use the Ultimate Research Assistant to eliminate writer's block and get your research paper done in record time. The Ultimate Research Assistant is a combination search engine and summarization tool for writers, students, educators, and researchers. It uses a combination of traditional search engine technology and text mining techniques to facilitate online research of complex topics. With the Ultimate Research Assistant, you can easily achieve a five-fold increase in productivity over traditional search engines when performing Internet research on complex topics. The Ultimate Research Assistant gives you access to the "collective intelligence" of the web when researching complex topics. Whether you are creating a research report for work, or a research paper, term paper or essay for school, the Ultimate Research Assistant will help you get your work done in record time. What makes the Ultimate Research Assistant different (and better) than existing search engines like Google is its ability to actually "read" the documents in the underlying search results and write a concise report summarizing your search topic. This saves you a significant amount of time, in that you don't have to click through pages of search results to find the nuggets of knowledge buried within multiple documents.
Views: 1718 UltimateResearchAsst
What is Text Mining?
 
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The introduction of Text Mining-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 2931 Jian Cui
The Cortical Engine for Processing Text
 
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The CEPT-Retina produces semantic fingerprints of language and thereby represents a new fundamental alternative to capture the inner semantics of natural language. These fingerprints can represent words, documents or the information needs of users. This approach helps get more relevant search results and classify information more efficiently. The CEPT-Retina also enables the making of intelligent decisions based on human-generated text input. Words can be represented as fingerprints A picture of cats or dogs represents a good example of a traditional semantic fingerprint. An audio recording would be another example that captures a different (audio) dimension. These kinds of semantic representation are handled by image resp. sound analysis, a process that is computationally intensive but feasible for some applications. From Symbols to Numeric representations The words CAT and DOG are symbolic representations of the entities of cats and dogs. To give meaning to these symbols, we need a dictionary as we are unable to interpret the representations on themselves. The CEPT-Retina transforms the symbol for CAT into its semantic fingerprint shown below in red, the same for DOG shown in blue. The overlay of the 2 fingerprints enables direct (visual) comparison of semantic relatedness. Therefore, we refer to them as semantic fingerprints.
Views: 5053 cortical.io
Digital Text Mining
 
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Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
Final Year Projects | Text Clustering with Seeds Affinity Propagation
 
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Final Year Projects | Text Clustering with Seeds Affinity Propagation More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 870 Clickmyproject
Biomedical text mining using the Ultimate Research Assistant
 
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http://ultimate-research-assistant.com/ In this webcast, Andy Hoskinson, the founder of the Ultimate Research Assistant, shows you how to use his tool to perform biomedical text mining over the Internet. Why spend tens of thousands of dollars on specialized software tools when you can use the Ultimate Research Assistant for free over the Internet?
Views: 1503 UltimateResearchAsst
Peers in Discover Text
 
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Introduction to the role of peers and peer requests in DiscoverText.
Views: 219 texifter
Data Mining and Text Analytics - Quranic Arabic Corpus
 
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Presentation on the Quranic Arabic Corpus. by Ismail Teladia and Abdullah Alazwari.
Views: 903 Ismail Teladia
Text Analysis day 1 vid.m4v
 
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Get The Original Text/Article Here http://www.scribd.com/doc/237987991/Text-Analysis-1
A Primer on Text Mining for Business
 
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Part of the series "Big data for business", a course by Clement Levallois at EMLYON Business School.
Browserscope & SpriteMe
 
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Google Tech Talk September 17, 2009 ABSTRACT Presented by Lindsey Simon and Steve Souders. This talk covers two open source projects being released by Googlers. Browserscope (http://browserscope.org/) is a community-driven project for profiling web browsers. The goals are to foster innovation by tracking browser functionality and to be a resource for web developers. The current test categories include network performance, Acid 3, selectors API, and rich text edit mode. SpriteMe (http://spriteme.org/) makes it easy to create CSS sprites. It finds background images in the current page, groups images into sprites, generates the sprite image, recomputes CSS background-positions, and injects the sprite into the current page for immediate visual verification. SpriteMe changes the timeline of sprite development from hours to minutes. Lindsey Simon is a Front-End Developer for Googles User Experience team. Simon hails from Austin, TX where he slaved at a few startups, taught computing at the Griffin School, and was the webmaster for many years at the Austin Chronicle. He currently lives in San Francisco and runs a foodie website dishola.com. Steve Souders works at Google on web performance and open source initiatives. Steve is the author of High Performance Web Sites and Even Faster Web Sites. He created YSlow, the performance analysis plug-in for Firefox. He serves as co-chair of Velocity, the web performance and operations conference from O'Reilly, and is co-founder of the Firebug Working Group. He recently taught CS193H: High Performance Web Sites at Stanford University. The video of this talk will be posted as part of the Web Exponents speaker series ( http://googlecode.blogspot.com/2009/05/web-e-x-ponents.html )
Views: 7493 GoogleTechTalks
Data Wrangling Normalization & Preprocessing: Part II Text
 
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Dr. Sanda Harabagiu from University of Texas at Dallas presents a lecture on "Data Wrangling, Normalization & Preprocessing: Part II Text." Lecture Abstract Data wrangling is defined as the process of mapping data from an unstructured format to another format that enables automated processing. State of the art deep learning systems require vast amounts of annotated data to achieve high performance, and hence, this is often referred to as a Big Data problem. Many decision support systems in healthcare can be successfully automated if such big data resources existed. Therefore, automated data wrangling is crucial to the application of deep learning to healthcare. In this talk, we will discuss data wrangling challenges for physiological signals commonly found in healthcare, such as electroencephalography (EEG) signals. For signal and image data to be useful in the development of machine learning systems, identification and localization of events in time and/or space plays an important role. Normalization of data with respect to annotation standards, recording environments, equipment manufacturers and even standards for clinical practice, must be accomplished for technology to be clinically relevant. We will specifically discuss our experiences in the development of a large clinical corpus of EEG data, the annotation of key events for which there is low inter-rater agreement (such as seizures), and the development of technology that can mitigate the variability found in such clinical data resources. In a companion talk to be given on December 2, data wrangling of unstructured text, such as that found in electronic medical records, will be discussed. View slides from this lecture https://drive.google.com/open?id=0B4IAKVDZz_JUV19oZElTUjI3RWs About the speaker Sanda Harabagiu is a Professor of Computer Science and the Erik Jonsson School Research Initiation Chair at the University of Texas at Dallas. She is also the Director of the Human Language Technology Research Institute at University of Texas at Dallas. She received a Ph.D. degree in Computer Engineering from the University of Southern California in 1997 and a Ph.D. in Computer Science from the University of Rome, “Tor Vergata”, Italy in 1994. She is a past recipient of the National Science Foundation Faculty Early CAREER Development Award for studying coreference resolution. Her research interests include Natural Language Processing, Information Retrieval, Knowledge Processing, Artificial Intelligence and more recently Medical Informatics. She has been interested for a long time in Textual Question-Answering, reference resolution and textual cohesion and coherence. In 2006 she co-edited a book entitled “Advances in Open Domain Question Answering”. Prof. Harabagiu is a member of AMIA, AAAI, IEEE and ACM. See www.hlt.utdallas.edu/~sanda to learn more about her research and teaching. Sanda Harabagiu is a co-PI on an NIH BD2K grant titled “Automatic discovery and processing of EEG cohorts from clinical records” which is a collaboration between Temple University and the University of Texas at Dallas. Join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
How to Make an Image Classifier - Intro to Deep Learning #6
 
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We're going to make our own Image Classifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing. Coding challenge for this video: https://github.com/llSourcell/how_to_make_an_image_classifier Charles-David's winning code: https://github.com/alkaya/TFmyValentine-cotw Dalai's runner-up code: https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating More Learning Resources: http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/ https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ http://cs231n.github.io/convolutional-networks/ http://deeplearning.net/tutorial/lenet.html https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ http://neuralnetworksanddeeplearning.com/chap6.html http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/ http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/ https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2 Join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 142703 Siraj Raval
TextDB: Declarative and Scalable Text Analytics on Large Data Sets
 
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Speaker: Chen Li Title / Affiliation: Professor, School of Information and Computer Sciences University of California, Irvine Talk Abstract: We are developing an open source system called "TextDB" for text analytics on large data sets. The goal is to build a text-centric data-management system to enable declarative and scalable query processing. It supports common text computation as operators, such as keyword search, dictionary-based matching, similarity search, regular expressions, and natural language processing. It supports index-based operators without scanning all the documents one by one. These operators can be used to compose more complicated query plans to do advanced text analytics. In the talk we will give an overview of the system, and present details about these operators and query plans. We will also report our initial results of using the system to do information extraction. The system is available at https://github.com/TextDB/textdb/wiki Biography: Chen Li is a professor in the Department of Computer Science at the University of California, Irvine. He received his Ph.D. degree in Computer Science from Stanford University in 2001, and his M.S. and B.S. in Computer Science from Tsinghua University, China, in 1996 and 1994, respectively. His research interests are in the field of data management, including data cleaning, data integration, data-intensive computing and text analytics. He was a recipient of an NSF CAREER Award, several test-of-time publication awards, and many other grants and industry gifts. He was once a part-time Visiting Research Scientist at Google. He founded a company SRCH2 to develop an open source search engine with high performance and advanced features from ground up using C++. About the Forum: The IBM THINKLab Distinguished Speaker Series brings together IBM and external researchers and practitioners to share their expertise in all aspects of analytics. This global bi-weekly event features a wide range of scientific topics which appeal to a broad audience interested in the latest technology for analytics, and how analytics is being used to gain insights from data.
Views: 398 IBM Research
Parsa Ghaffari - Football (Soccer) Highlights (Part 1)
 
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Parsa Ghaffari's performance; Daily Training sessions & games
Views: 249 Parsa Ghaffari
Korilog/Knime Extension: use case Yersinia functional genomics
 
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A video showing how to setup a simple yet powerful workflow to do comparative and functional genomics of various Yersinia species.
Views: 181 korilog56
DBSCAN Clustering for Identifying Outliers Using Python - Tutorial 22 in Jupyter Notebook
 
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In this tutorial about python for data science, you will learn about DBSCAN (Density-based spatial clustering of applications with noise) Clustering method to identify/ detect outliers in python. you will learn how to use two important DBSCAN model parameters i.e. Eps and min_samples. Environment used for coding is Jupyter notebook. (Anaconda) This is the 22th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets. Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 9160 TheEngineeringWorld
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings 2012 IEEE PROJECT
 
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Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings 2012 IEEE PROJECT TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com
Views: 601 jpinfotechprojects
Data Mining - "Look Ma, No hands!" A Parameter-Free Topic Model | Lectures On-Demand
 
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Jian Tang School of Information - University of Michigan School of EECS - Peking University The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Web Data Mining To Detect Online Spread Of Terrorism
 
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Get this project at http://nevonprojects.com/web-data-mining-to-detect-online-spread-of-terrorism/ Detects terrorism related web pages and flags them using datamining on web pages
Views: 8366 Nevon Projects
AntWordProfiler (AWP) - Overview
 
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This screencast shows some of the basic features of AntWordProfiler. You can support this work at: http://www.patreon.com/antlab
Views: 6427 Laurence Anthony
IEEE Projects | Efficient Mining of Frequent Itemsets on Large Uncertain Databases
 
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IEEE Projects | Efficient Mining of Frequent Itemsets on Large Uncertain Databases More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 510 Clickmyproject
Mozenda Rescues Duluth Pack's Data, Allowing Switch to Magento,web data extractor
 
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Mozenda and Duluth Pack featured on PR Video: https://www.prvideo.com Duluth, MN (Vocus) September 9, 2010 Anyone who's worked in e-commerce knows how frustrating and time-consuming it can be to change your online storefront from one platform to another. But what can you do when the old platform is actually holding your information hostage? This was the situation facing Duluth Pack, a Minnesota-based company that has been making rugged, high-quality outdoor gear since 1882. They wanted to switch their online storefront to Magento, an up-and-coming e-commerce platform whose quality and versatility suited the iconic business nicely. But Duluth Pack's website aggregated information from several different sources, and their database was stored securely behind the firewall of the web development company they were leaving. So securely, in fact, that Duluth Pack couldn't access it. It was enough to make any self-respecting outdoorsman want to disappear into the Boundary Waters of Northern Minnesota and swear off technology indefinitely. Or maybe just swear. Luckily, Duluth Pack's marketing department stumbled across Mozenda, a powerhouse web data mining tool that was able to retrieve and organize all the information associated with duluthpack.com. "At first, the software seemed too good to be true," Duluth Pack's marketing director Abe Pattee said. "Mozenda was able to harvest all the data from duluthpack.com and replicate our database without ever seeing it, while integrating all of our product reviews." Duluth Pack built an off-site back-up system with the information provided by Mozenda, then made the switch to Magento. The change has been a good one for Duluth Pack; they have enjoyed 115% growth in the year since they switched to Magento. They have also found new uses for Mozenda, using it to aggregate supplier information directly into their storefront. Mozenda and Magento have played a vital role in helping the 127-year-old outdoor supplier maintain the robust health and fresh-faced image it is known for, and in making the iconic Duluth Pack available to a whole new generation of people, whether they use it to portage hard tack and bug spray across the deep lakes and pine-scented forests of Northern Minnesota, or to tote their laptops through urban jungles Duluth Pack's founders could never have imagined. Maybe technology isn't so bad after all, even for the most hard-core outdoorsy-types. About Duluth Pack Duluth Pack has been manufacturing quality canvas and leather goods in Duluth, Minnesota, since 1882. The company makes everything from its original canoe packs to high-quality canvas and leather luggage, business gear, school bags, hunting gear and more. Every pack they make comes with a lifetime guarantee, a testament to the commitment to quality they've maintained for 127 years. For more information about Duluth Pack visit http://www.duluthpack.com About Mozenda Mozenda is a Salt Lake City tech firm specializing in the development and sale of web data extraction software tools - http://www.mozenda.com. Its cornerstone product is the patent-pending Web Agent Builder 2.0, a web data extraction tool that grabs precise website content and organizes it into useable formats.
Views: 580 PRVideoDotCom
Data Mining - Analysis of Information Needs on Twitter | Lectures On-Demand
 
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Zhe Zhao Student, Computer Science and Engineering - University of Michigan Faculty, School of information - University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Operator's Social Media Data Analysis (using MicroStrategy)
 
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Welcome to Demo on Sentiment Analysis using Operator's Social Media Data Background & Purpose The purpose of the application is to analyze the customer sentiment (positive, neutral or negative) towards the services of two major Indian Operators -- Vodafone & Airtel. The application makes use of social media data of these Telco's for Analysis. Operators use social media platform as a promotional channel to increase awareness of their products and services and also, to address complaints of customers. Customer comes to these Facebook and Twitter pages to highlight any problems they are facing with the service. Since, social media posts have a high chance of going viral through re-post and re-tweets, hence a dedicated team is assigned to analyze all posts so as to resolve the customer complaints in a timely manner. The application would be of help to operators to do a comparative analysis of how their service is perceived by customer w.r.t to that of their competitors and draw actionable insights of the areas where they need to focus on. For example, geographical areas where they need to improve their network revise existing rate plans or offer new products. Data The data in the application is scrapped from Twitter and Facebook pages of Vodafone and Airtel. Analytical tool 'R' is then used to process this data to identify the sentiment of post/tweet and determine the category of the post such as Rate Plan, Billing, Network etc. Processed data is downloaded in form of an xls and used by MicroStartegy Analytic Desktop for Analysis (open xls attachments). Metrics like 'City' from where post was made, Number of followers, Page Rank of the posters are also captured. Analysis I shall now walk you through each of the dashboards(twitter, facebook) and describe the various visualizations. There are principally two dashboards in the application that provides ability to do analysis on Vodafone and Airtel social media by criteria such as - • Location • Influencer • Post Category I hope you have enjoyed Sentiment Analysis of Operator's Social media data. Thank you for taking time to watch this demo. Please also have a look at some of other demos as well.
Views: 1338 Rajat Mehta
Preparing Students for Successful Careers with Predictive Analytics - IBM's Stephen Gold
 
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IBM's Stephen Gold, business unit executive, global education, discusses the value of having skills in predictive analytics, a significant differentiator upon graduation and increasingly in demand by employers around the globe. As organizations from all industries seek to create value from the exponentially growing amount of structured and unstructured data, they need leaders with strong analytical capabilities to understand this data for smarter decisions and improved performance.
Views: 1721 timjpowers
Class Project 2
 
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Class Project for Introduction to Data Mining.
Views: 131 Eric Frohnhoefer
The new Nengo GUI
 
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Nengo now has a reworked user interface that provides a live-coding interface to building neural models. As an example, this video shows the construction of a minimal example model that has extremely basic sensory, motor, memory, and cognitive control systems. This is a simple critter that has a sensory input that indicates the direction food is in, and an output of what direction the agent wants to go. There is also an input that "scares" the critter. If the critter is not scared, it should go in the direction of the food. If the critter is scared, it should run back to wherever it started from. In order to do this, it needs a memory of where it is, and to update that memory based on its movement. To achieve this, we need a neural model that can compute the integral of the motor output to get an estimate of the current position, and we need to selectively gate the signal going to the motor system such that it is the food direction if not scared, and the negative of the current position if it is scared.
Views: 1294 CTNWaterloo
Smart Trader (Twitter Datamining for Profiting on Real Time News Events)
 
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Discussion: A week or so ago I posted a message to our free usergroup (linked below) about a Predictive Market Analysis concept that could be used in conjunction with Smart Volume Analysis using real time activity from social media sites such as twitter. Such a system might be used to exploit news events for profit. A proof of concept example such as the one shown here could be the basis for such a system. The idea is to let machines score hashtags by region as they come in. Example. An Earthquake just hit Japan the system would start receiving a large number of related hashtags and once it breached a threshold it would alert you that an Earthquake likely just hit Japan. You could then open or close trades accordingly. Join us to discuss Smart Volume Analysis and Trading (Free) Here: https://plus.google.com/communities/105387595221569368907 Note: The video is a visual representation of how the system might look/work only. (Music is original and was created by me using Reason)
Views: 967 Smart Traders
Data Analysis with Python and Pandas Tutorial Introduction
 
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Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library. At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds. Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/ Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/ Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/
Views: 452370 sentdex
Running a KMeans Cluster Analysis
 
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Hello Friends this is Shivam and you are watching the thirteenth tutorial of this series of Data Analysis using Python. In this tutorial, we will explore how to run a K Means Cluster Analysis in Python. You can download the IPYNB Notebook of this tutorial from the following link: https://drive.google.com/drive/folders/0B9QuBDp5L8FaZjhUTVZmczZSYUE Github: https://github.com/ShivamPanchal Blog: dataenthusiasts.wordpress.com LinkedIn: https://www.linkedin.com/in/panchalshivam/ So, Enjoy Learning. And, Don't forget to like and Subscribe!!!! Thanks
Views: 275 Analytics Mantra
3. Entity Analysis in Unstructured Data
 
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RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: http://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner Historical evolution of the web and cloud computing. Using the exploded (D4M) schema. Analyzing computer network data. Analyzing computer network data. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 2313 MIT OpenCourseWare
Clustering dengan K Medoids dan Hierarchical
 
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Aldy Hernawan | 16.01.63.0027 Clustering lima belas Data Mahasiswa dengan 5 variabel (nama, TB, BB, Gender, Gol darah). Menggunakan K Medoids dan Hierarchical Clustering Music : Dont Worry Be Happy - Bob Marley
Views: 55 Aldy Hernawan
Indexing file system with Constellio 1.1
 
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Based on Apache Solr and using Google Search Appliances connectors architecture, it allows, with a single click, to find all relevant content in your organization (Web, email, ECM, CRM etc.). http://www.constellio.com
Views: 3796 Rida Benjelloun
IEEE 2011 JAVA Text Clustering with Seeds Affinity Propagation
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 356 PG Embedded Systems
Classification of Big Data Applications and Convergence of HPC and Cloud Technology (1/2)
 
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Keynote: Classification of Big Data Applications and Convergence of HPC and Cloud Technology Professor Geoffrey Fox, ACM Fellow, Indiana University, USA SKG2015: http://www.knowledgegrid.net/skg2015 Abstract We discuss study of the nature and requirements of many big data applications in terms of Ogres that describe important general characteristics. We develop ways of categorizing applications with features or facets that are useful in understanding suitable software and hardware approaches where 6 different broad paradigms are identified. This allows study of benchmarks and to understand when high performance computing (HPC) is useful. We propose adoption of DevOps motivated scripts to support hosting of applications on the many different infrastructures like OpenStack, Docker, OpenNebula, Commercial clouds and HPC supercomputers.   Bio Professor Fox is a distinguished professor of Informatics and Computing, and Physics at Indiana University where he is director of the Digital Science Center and Associate Dean for Research and Graduate Studies at the School of Informatics and Computing. He has supervised the Ph.D. of 61 students and published over 600 papers in physics and computer science. He currently works in applying computer science to Bioinformatics, Defense, Earthquake and Ice-sheet Science, Particle Physics and Chemical Informatics. He is principal investigator of FutureGrid - a new facility to enable development of new approaches to computing. Professor Fox is a Fellow of ACM.
Views: 91 Bill Xu
Rapid Recursive® Methodology
 
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Our Rapid Recursive® (patent-pending) methodology incorporates advanced mathematics and dynamic programming. This allows for robust financial and risk assessment models that integrate information on decision options, market conditions, and expected rates of return, as well as the knowledge and intuition of managers and decision makers. As a result, Rapid Recursive® models provide a superior approach to evaluating multi-period investment opportunities.
Subspace Clustering Using Log-determinant Rank Approximation
 
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Authors: Chong Peng, Zhao Kang, Huiqing Li, Qiang Cheng Abstract: A number of machine learning and computer vision problems, such as matrix completion and subspace clustering, require a matrix to be of low-rank. To meet this requirement, most existing methods use the nuclear norm as a convex proxy of the rank function and minimize it. However, the nuclear norm simply adds all nonzero singular values together instead of treating them equally as the rank function does, which may not be a good rank approximation when some singular values are very large. To reduce this undesirable weighting effect, we use a log-determinant function as a non-convex rank approximation which reduces the contributions of large singular values while keeping those of small singular values close to zero. We apply the method of augmented Lagrangian multipliers to optimize this non-convex rank approximation-based objective function and obtain closed-form solutions for all subproblems of minimizing different variables alternatively. The log-determinant low-rank optimization method is used to solve subspace clustering problem, for which we construct an affinity matrix based on the angular information of the low-rank representation to enhance its separability property. Extensive experimental results on face clustering and motion segmentation data demonstrate the effectiveness of the proposed method. ACM DL: http://dl.acm.org/citation.cfm?id=2783303 DOI: http://dx.doi.org/10.1145/2783258.2783303

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