...Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text Mining Killian Thiel Tobias Kötter Dr. Michael Berthold Dr. Rosaria Silipo Phil Winters Killian.Thiel@uni-konstanz.de Tobias.koetter@uni-konstanz.de Michael.Berthold@uni-konstanz.de Rosaria.Silipo@KNIME.com Phil.Winters@KNIME.com Copyright © 2012 by KNIME.com AG all rights reserved Revision: 120403F page 1 Table of Contents Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text Mining............................................................................................................................................ 1 Summary: “Water water everywhere and not a drop to drink” ............................................................ 3 Social Media Channel-Reporting Tools. .................................................................................................. 3 Social Media Scorecards .......................................................................................................................... 4 Predictive Analytic Techniques ............................................................................................................... 4 The Case Study: A Major European Telco. ............................................................................................. 5 Public Social Media Data: Slashdot ....................................................................................................
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...See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/296695247 USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT ALCOHOL CONSUMPTION Dataset · February 2016 DOI: 10.13140/RG.2.1.1465.8328 READS 2,200 2 authors: Fabio Pagnotta Hossain Amran University of Camerino University of Camerino 8 PUBLICATIONS 0 CITATIONS 5 PUBLICATIONS 0 CITATIONS SEE PROFILE All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. SEE PROFILE Available from: Hossain Amran Retrieved on: 12 April 2016 USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT ALCOHOL CONSUMPTION Fabio Pagnotta Mat:-093579 Mohammad Amran Hossain Mat:-093192 Department of Computer science, University of Camerino Advanced Database In this project, we use a data set about Portuguese student on two courses ( Mathematics and Portuguese ) which was collected and analysed by Paulo Cortez and Alice Silva, University of Minho,Portugal. Our work intends to approach student addiction on alcohol in secondary level using business intelligence (BI) and Data Mining (DM) techniques. The result shows that a good predictive accuracy can be achieved, provided that addiction of alcohol can impact to the student performance. In addition,the result also provides the correlation between alcohol usage and the social, gender and study time attributes...
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...Rexer Analytics 4th Annual Data Miner Survey – 2010 Survey Summary Report – For more information contact Karl Rexer, PhD krexer@RexerAnalytics.com www.RexerAnalytics.com Outline • Overview & Key Findings • Where & How Data Miners Work • What’s Important to Data Miners • Data Mining Tools: Usage & Satisfaction • Overcoming Challenges & Optimism about the Future • Appendix: Where do Data Miners Come From? • Appendix: Rexer Analytics © 2011 Rexer Analytics 2 Overview & Key Findings © 2011 Rexer Analytics 3 2010 Data Miner Survey: Overview Vendors Corporate • Fourth annual survey NGO / Gov’t • 50 questions • Data collected online in early 2010 Academics Consultants • 10,000+ invitations emailed, plus promoted by newsgroups, vendors, and bloggers • Respondents: 735 data miners from 60 countries Note: Data from tool vendors (companies making data mining software) was excluded from many analyses. © 2011 Rexer Analytics Central & South America (4%) • Columbia 2% • Brazil 1% Asia Pacific • India 4% • Australia 3% • China 2% Middle East & Africa (3%) • Israel 1% • Turkey 1% North America • USA 40% • Canada 4% Europe • Germany 7% • UK 5% • France 4% • Poland 4% 4 Key Findings • FIELDS & GOALS: Data miners work in a diverse set of fields. CRM / Marketing has been the #1 field in each of the past four years. Fittingly, “improving the understanding of customers”...
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...1.0 Introduction Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. It describes the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies committed to data-driven decision making. It focuses on developing new insights and understanding of business performance based on data and statistical methods. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration...
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...Decision Support Systems 50 (2011) 491–500 Contents lists available at ScienceDirect Decision Support Systems j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d s s Detection of financial statement fraud and feature selection using data mining techniques P. Ravisankar a, V. Ravi a,⁎, G. Raghava Rao a, I. Bose b a b Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057, AP, India School of Business, The University of Hong Kong, Pokfulam Road, Hong Kong a r t i c l e i n f o a b s t r a c t Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. © 2010 Elsevier B.V. All rights reserved. Article history: Received 20 November 2009 Received in revised form 14 June 2010 Accepted 3 November 2010 Available online 12 November 2010 Keywords: Data mining...
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