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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: 1996 ironfrown
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: 995 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: 1388 ironfrown
04 Importing Data in RapidMiner Studio
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 15221 RapidMiner, Inc.
03 Visualizing Data in RapidMiner Studio
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 11945 RapidMiner, Inc.
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: 1433 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: 420 ironfrown
Lu Chen: Mining and Analyzing Subjective Experiences in User Generated Content
 
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Lu Chen's Dissertation Defense: " Mining and Analyzing Subjective Experiences in User Generated Content " Tuesday, August 9, 2016 Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau Home Page: http://knoesis.org/researchers/luchen/ Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732 Slides: https://www.slideshare.net/knoesis/mining-and-analyzing-subjective-experiences-in-usergenerated-content ABSTRACT: Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies. Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components. We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phrases of different lengths and slang expressions including abbreviations and spelling variations. We then look into the task of finding opinion targets in product reviews, where the product features (product attributes and components) are usually the targets of opinions. We propose a clustering approach that identifies product features and groups them into aspect categories. Finally, we study the classification and assessment of several types of subjective information (e.g., sentiment, political preference, subjective well-being) in two specific application scenarios. One application is to predict election results based on analyzing the sentiments of social media users towards election candidates. Observing that different political preference and tweeting behavior of users may have significant effect on predicting election results. We propose methods to group users based on their political preference and participation in the discussion, and assess their sentiments towards the candidates to predict the results. We examine the predictive power of different user groups in predicting the results of 2012 U.S. Republican Presidential Primaries. The other application is to understand the relationship between religiosity and subjective well-being (or happiness). We analyze the tweets and networks of more than 250k U.S. Twitter users who self-declared their beliefs. We build classifiers to classify believers of different religions using the self-declared data. In order to understand the effect of religiosity on happiness, we examine the pleasant/unpleasant emotional expressions in users' tweets to estimate their subjective well-being, and investigate the variations in happiness among religious groups.
Views: 183 Knoesis Center
Creating custom classification models - text analytics
 
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https://text2data.com Apply advanced Machine Learning methods to document categorization. Simply train the models and use it in your analysis.
Views: 654 Sentiment Analysis
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: 1528 UltimateResearchAsst
Semantic Text Processing: Example Application
 
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This example application shows how Semantic Technology and Text Processing (Text Mining) can be used together to deliver semantic text processing. This application compares two legal documents (e.g. service contracts) and referring to the word semantics (actual meaning) looks for sentences that mean the same. This could not be achieved only with statistical methods and keyword-based comparison. This demo application was build using Ontorion Server SDK. To learn more visit: http://www.cognitum.eu/semantics/
Views: 790 cognitumeu
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.
Textual Analysis Tool
 
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Simple textual analysis tool. Retrieves text from Google searches and PDF files and runs textual analysis. Can also perform semantic textual analysis. In its extended version the tool can gather news and other type of data from multiple sources and perform various types of textual analysis which can be used in investments. Parameters are takes from MS Excel (*.xlsx) files.
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: 2354 YaleUniversity
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/
S1E8 of 5 Minutes With Ingo: Cross Validation in Practice
 
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In this episode, our resident RapidMiner masterminds, Ingo Mierswa & Simon Fischer, spend some quality time together building a cross validation process on Fisher’s Iris data set (name pun intended). Seeing as Ingo had recently talked about cross-validation, today he and Simon quickly design the complete process and discuss what this important method looks like in practice. Simon then jumps in and gives a guest lecture on a so-called "error matrix" and together the boys cover key performance metrics including precision, recall, and accuracy. At that point, they realize how much coding effort originally went into this series of implementations which leads to Ingo's strange daydream about Code Club. But we're not to supposed to talk about that. Plus, Data Scientist #7 asks "where is my mind" as he reads Prolog by Hans Kleine Büning & Stefan Schmitgen. MUSIC CREDITS: Intro: Where Is My Mind, Pixies, Surfa Rosa, Elektra Entertainment Group/4AD, 1999 / 2003 Outro: Medulla Oblongata, The Dust Brothers, Fight Club Soundtrack, Restless Records, 1999
Views: 2213 RapidMiner, Inc.
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
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: 1723 UltimateResearchAsst
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
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In this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 22291 Data Science Dojo
I can do text analytics!
 
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Full Title: I can do text analytics!: designing development tools for novice developers Authors: Huahai Yang, Daina Pupons-Wickham, Laura Chiticariu, Yunyao Li, Benjamin Nguyen, Arnaldo Carreno-Fuentes Abstract: Text analytics, an increasingly important application domain, is hampered by the high barrier to entry due to the many conceptual difficulties novice developers encounter. This work addresses the problem by developing a tool to guide novice developers to adopt the best practices employed by expert developers in text analytics and to quickly harness the full power of the underlying system. Taking a user centered task analytical approach, the tool development went through multiple design iterations and evaluation cycles. In the latest evaluation, we found that our tool enables novice developers to develop high quality extractors on par with the state of art within a few hours and with minimal training. Finally, we discuss our experience and lessons learned in the context of designing user interfaces to reduce the barriers to entry into complex domains of expertise. URL: http://dl.acm.org/citation.cfm?id=2466212 DOI: http://dx.doi.org/10.1145/2470654.2466212
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: 402 IBM Research
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: 1359 Rajat Mehta
Interactive Machine Learning Visualizations with Domino
 
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Businesses increasingly use machine-learning models to recognize patterns in big data and to implement data-driven decision-making. In this webinar, you will learn how Domino serves as a platform for experimentation and collaboration, and facilitates the creation and distribution of machine-learning models. We will give you an introduction on how to use Plotly—an interactive data visualization tool—to share the results from your models more effectively. We will also show you how to use Plotly's API libraries in Domino Data Lab to build insightful graphs, charts and data visualizations in Python and in R.
Views: 1855 Domino Data Lab
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 163406 Siraj Raval
TIMi 2: developping simple churn prevention models
 
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TIMi predictive datamining software: introduction to churn models with the standard TIMi interface
Views: 1008 TIMi
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: 7501 GoogleTechTalks
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: 928 Ismail Teladia
K-means clustering: how it works
 
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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 510920 Victor Lavrenko
Predictive Modeling of Retention at Dickinson College: 3 of 4 - Predictive Model Building
 
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Retention modeling for higher education can be quite challenging in the best of conditions. However, predicting who will retain and who will not can be even more difficult if your school is relatively small and has a relatively high retention rate. Couple this condition with the very dynamic environment we are currently experiencing in higher education and you have an even more demanding task. In this presentation, Dr. Johnson, Director of Institutional Research at Dickinson College, provides details for the methodology used by Dickinson College to create a first-year retention model using Rapid Insight Analytics software. The results are discussed to some degree, highlighting the fact that sometimes what you don't find in the model can be almost as useful as what you do.
Views: 705 Rapid Insight Inc.
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
Lecture - 34 Rule Induction and Decision Trees - I
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 19087 nptelhrd
Discovering Content by Mining the Entity Web - Part 5 of 6
 
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Deep Dhillon, CTO of Evri.com presents Evri's technology to UW students at the Paul G. Allen Center for Computer Science & Engineering. Talk abstract: Unstructured natural language text found in blogs, news and other web content is rich with semantic relations linking entities (people, places and things). At Evri, we are building a system which automatically reads web content similar to the way humans do. The system can be thought of as an army of 7th grade grammar students armed with a really large dictionary. The dictionary, or knowledge base, consists of relatively static information mined from structured and semi-structured publicly available information repositories like Freebase, Wikipedia, and Amazon. This large knowledge base is in turn used by a highly distributed search and indexing infrastructure to perform a deep linguistic analysis of many millions of documents ultimately culminating in a large set of semantic relationships expressing grammatical SVO style clause level relationships. This highly expressive, exacting, and scalable index makes possible a new generation of content discovery applications. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 192 zang0
Evaluating Similarity Measures: A Large-Scale Study in...
 
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Google TechTalk June 21, 2006 Ellen Spertus is a Software Engineer at Google and an Associate Professor of Computer Science at Mills College, where she directs the graduate program in Interdisciplinary Computer Science. She earned her bachelor's, master's, and doctoral degrees from MIT, and has done research in parallel computing, text classification, information retrieval, and online communities. She is also known for her work on women and computing and various odd adventures, which have led to write-ups in The Weekly World News and other fine publications. ABSTRACT As online information services grow, so does the need and opportunity for automated tools to help users find information of...
Views: 1983 Google
Process Mining Quiz Solution - Georgia Tech - Health Informatics in the Cloud
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud809/l-1618138700/e-1634868735/m-1634868738 Check out the full Health Informatics course for free at: https://www.udacity.com/course/ud809 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 80 Udacity
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: 96 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.
Modeling Forest Fire Occurence in Riau Province, Indonesia using Data Mining Method
 
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Ms. Imas Sukaeshi Sitanggang Lecturer, Faculty of Natural Science and Mathematics, Bogor Agricultural University, Indonesia
Views: 196 SEAMEO SEARCA
SAS Visual Investigator - The 60-Second Scoop
 
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SAS Visual Investigator helps intelligence analysts make connections, find patterns and realize relationships between disparate data sources. Product marketing manager Brooke Fortson describes the product in 60 seconds. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 2028 SAS Software
Digital Marketing Tips, Prototype Tools, a Content Generator & More | Growth Insights #15
 
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Welcome back to Growth Insights #15! David takes us through the best digital marketing tips from the last month, including a headline content generator and amazing prototype tools and web design tools! Get the latest tools list: https://grow.ac/issue15 Ever wanted Slack integrations to help you to search for things on Wikipedia without ever having to navigate away from the app? Maybe you’re wondering exactly how Russian propaganda is linked to the outcome of 2016’s American election. (Hint: It involves 2.700 fake Facebook accounts and 80.000 posts.) Or perhaps you're interested in web development tools? Well, we’ve got you covered with our essential growth tools toolkit. You’ll never have to guess about whether or not your new logo design will look good in real-life with a prototype tool, and you code your own self-destructing Banksy (we’d recommend using a photo of your most annoying colleague) with a brilliant web development tool. Think that visual search is still a distant dream? Not anymore - Snapchat just integrated it so that you can snap a product and buy the same product (or the nearest match to it on Amazon). And if you think that hardware hacks are old-school, factories in China were discovered to have nested tiny microchips, no bigger than a grain of rice, on the motherboards of expensive US servers. We're not suggesting this as one of our digital marketing tips, but it's interesting to know how they did it! Finally, don’t forget the headline content generator tool - simply pop in a keyword and instantly receive tonnes of headlines to help you create your content! So, there you have it! All the best digital marketing tips and tools we discovered this month, including prototype tools, a headline content generator, a few project management tools and a sprinkling of old-school hacking. What more do you need? Let us know in the comments if you think we've missed any marketing resources or awesome growth hacking tools! ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/school/growt... Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Video URL: https://youtu.be/JYVprUQH2Fo
Views: 4130 Growth Tribe
PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency
 
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PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency Hwanjun Song (KAIST) Jae-Gil Lee (KAIST) Wook-Shin Han (POSTECH) The k-medoids algorithm is one of the best-known clustering algorithms. Despite this, however, it is not as widely used for big data analytics as the k-means algorithm, mainly because of its high computational complexity. Many studies have attempted to solve the efficiency problem of the k-medoids algorithm, but all such studies have improved efficiency at the expense of accuracy. In this paper, we propose a novel parallel k-medoids algorithm, which we call PAMAE, that achieves both high accuracy and high efficiency. We identify two factors—-“global search” and “entire data”—-that are essential to achieving high accuracy, but are also very time-consuming if considered simultaneously. Thus, our key idea is to apply them individually through two phases: parallel seeding and parallel refinement, neither of which is costly. The first phase performs global search over sampled data, and the second phase performs local search over entire data. Our theoretical analysis proves that this serial execution of the two phases leads to an accurate solution that would be achieved by global search over entire data. In order to validate the merit of our approach, we implement PAMAE on Spark as well as Hadoop and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). The results show that PAMAE significantly outperforms most of recent parallel algorithms and, at the same time, produces a clustering quality as comparable as the previous most-accurate algorithm. The source code and data are available at https://github.com/jaegil/k-Medoid. More on http://www.kdd.org/kdd2017/
Views: 743 KDD2017 video
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 89070 Skillshare
The Next Big Move: YELP Special Coverage Big Breakout 2014 Trading Analysis
 
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The Next Big Move: YELP Special Coverage Big Breakout 2014 Trading Analysis (VIDEO). Learn how to make money trading shares of YELP in 2014. Internet stocks are back in flavor and shares of YELP closed Friday at 82.21 Up 3.79 or (4.83%) up over 4x from it's 52 week low price of $19.45. Free Trial Signup http://www.stockmarketfunding.com/Free-Trading-Seminar Follow us on Facebook: http://www.facebook.com/OnlineTradingPlatform Follow us on Twitter https://twitter.com/TradeEducation Find Us on Google +1 http://gplus.to/TradingStocks Join us on Linkedin http://www.linkedin.com/groups/StockMarketFunding-Pro-Traders-1143227 Email Signup http://www.stockmarketfunding.com/evideosignup.htm Tags "stock market news" "stock market" "stock market data" "stock market report" "stock markets" "stock markets" "stock market today" "nasdaq stock market" "stock market quotes" "world stock market" "stock market ticker" "stock market index" "stock market watch" "stock market cycles" "stock market charts" "stock market results" "stock market software" "stock market hours" "stock market trends" "stock market prices" "stock market analysis" "us stock market" "live stock market" "stock market" "high frequency trading" "technical analysis" "hft trading" "option trading" "options trading" "stock market tutorial" "stock market technical analysis" "how to trade options" "how to trade stocks" "stock market funding" stockmarketfunding.com "Options Trading Strategies" "Options Trading" "Stock Chart Technical Analysis" "Stock Technical Analysis" "Options Trading Education Video " "Stock Market Trading" "Stock Market Trading Education" "Stock Market Trading Account" "Stock Market Trading Education" "Stock Options Trading" "Stock Options Trading Education" "Stock Market" "Stock Trading" "Online Trading" "Day Trading" "Swing Trading" "Technical Analysis" "Stock Trade" "Options Trade" Investing Trader Market Markets Invest Investment "US Stocks" "Stock Market Trade" "High Frequency Trading"
Object Detection and Classification from Large-Scale Cluttered Indoor Scans
 
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This is a work co-authored by Oliver Mattausch Daniele Panozzo, Claudio Mura, Olga Sorkine-Hornung, Renato Pajarola. The paper has been presented at Eurographics 2014. We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly-planar patches related by geometric transformations. This representation enables us to robustly filter out noise and greatly reduces the computational cost and memory requirements of our method, enabling execution at interactive rates. We propose a patch similarity measure based on shape descriptors and spatial configurations of neighboring patches. The patches are clustered in a Euclidean embedding space based on the similarity matrix to yield the segmentation of the input point cloud. The generated segmentation can be used to compress the raw point cloud, create an object database, and increase the clarity of the point cloud visualization.
Views: 287 Oliver Mattausch
Delhi votes: Twitter sentiment analysis
 
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The voting for the 70-member Delhi Assembly began at 8 AM on Saturday. With a high-pitched, bitterly contest election campaign behind, the fate of 673 candidates which includes 63 women in now in the hands of 1.30 crore voters. Here we bring to you the sentiment analysis on twitter. Like us on Facebook, follow us on Twitter and subscribe to our channel on YouTube Facebook https://www.facebook.com/cnnibn Twitter https://twitter.com/ibnlive YouTube https://www.youtube.com/user/ibnlive
Views: 236 CNN-News18
Natural language processing tools thesis
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topics-in-computer-science/
Views: 139 PHD PROJECTS
What is CANCER CLUSTER? What does CANCER CLUSTER mean? CANCER CLUSTER meaning & explanation.
 
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What is CANCER CLUSTER? What does CANCER CLUSTER mean? CANCER CLUSTER meaning - CANCER CLUSTER definition - CANCER CLUSTER explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A cancer cluster is a disease cluster in which a high number of cancer cases occurs in a group of people in a particular geographic area over a limited period of time. Historical examples of work-related cancer clusters are well documented in the medical literature. Notable examples include: scrotal cancer among chimney sweeps in 18th century London; osteosarcoma among female watch dial painters in the 20th century; skin cancer in farmers; bladder cancer in dye workers exposed to aniline compounds; and leukemia and lymphoma in chemical workers exposed to benzene. Cancer cluster suspicions usually arise when members of the general public report that their family members, friends, neighbors, or coworkers have been diagnosed with the same or related cancers. State or local health departments will investigate the possibility of a cancer cluster when a claim is filed. In order to justify investigating such claims, health departments conduct a preliminary review. Data will be collected and verified regarding: the types of cancer reported, numbers of cases, geographic area of the cases, and the patients clinical history. At this point, a committee of medical professionals will examine the data and determine whether or not an investigation (often lengthy and expensive) is justified. In the U.S., state and local health departments respond to more than 1,000 inquiries about suspected cancer clusters each year. It is possible that a suspected cancer cluster may be due to chance alone; however, only clusters that have a disease rate that is statistically significantly greater than the disease rate of the general population are investigated. Given the number of inquiries it is likely that many of these are due to chance alone. It is a well-known problem in interpreting data that random cases of cancer can appear to form clumps that are misinterpreted as a cluster. A cluster is less likely to be coincidental if the case consists of one type of cancer, a rare type of cancer, or a type of cancer that is not usually found in a certain age group. Between 5% to 15% of suspected cancer clusters are statistically significant.
Views: 79 The Audiopedia
Predicting the stock market with big-data analysis of tweets
 
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Big data analysis of Tweets can be used to predict developments in the stock market in the short and the long term. Research by RSM's Ting Li and Jan van Dalen. Read more on RSM Discovery: https://discovery.rsm.nl/articles/detail/320-predicting-the-stock-market-with-big-data-analysis-of-tweets/ Read the paper Li, T., van Dalen, J. & van Rees, P.J., More than just noise? Examining the information content of stock microblogs on financial markets, J Inf Technol (2017): https://link.springer.com/article/10.1057%2Fs41265-016-0034-2