Social Networks in Data Mining: Challenges and Applications
|
|
- Norma Long
- 8 years ago
- Views:
Transcription
1 Social Networks in Data Mining: Challenges and Applications SAS Talks May 10, 2012 PLEASE STAND BY Today s event will begin at 1:00pm EST. The audio portion of the presentation will be heard through your computer speakers. This is an automatic setup and is preferred. There will also be a limited option to listen through the telephone to 250 lines. If you would prefer to dial in, please call: US Toll-Free: Toll/International: Conference Code: # If you experience any technical difficulties, you may contact WebEx Technical Support at #sastalks 1 Copyright 2012, SAS Institute Inc. All rights reserved.
2 Social Networks in Data Mining: Challenges and Applications SAS Talks May 10, 2012 Copyright 2012, SAS Institute Inc. All rights reserved.
3 Speakers Stacy Hobson Director, Customer Loyalty and Retention SAS Institute Bart Baesens Associate Professor, K.U. Leuven (Belgium) Lecturer, University of Southampton (United Kingdom) 3 Copyright 2012, SAS Institute Inc. All rights reserved.
4 Social Networks in Data Mining: Challenges and Applications Prof. dr. Bart Baesens 1 Dr. Wouter Verbeke 2 1,2 Department of Decision Sciences and Information Management K.U.Leuven (Belgium) 1 Vlerick Leuven Ghent Management School (Belgium) 1 School of Management University of Southampton (United Kingdom) {Bart.Baesens;Wouter.Verbeke}@econ.kuleuven.be Twitter: DataMiningApps Facebook: Data Mining with Bart
5 My Research Team process mining business process management data mining (social) network analysis incorporating domain knowledge in classification models customer churn prediction data quality in a credit risk management context data quality and decision making data quality metrics Helen.Moges@econ.kuleuven.be customer churn prediction social network analysis profit based data mining Thomas.Verbraken@econ.kuleuven.be Wouter.Verbeke@econ.kuleuven.be credit risk modeling and scoring rating transitions microfinance survival analysis Philippe.Louis@econ.kuleuven.be machine learning in software engineering: software fault & effort prediction comprehens. decision supportive data modeling systems Karel.Dejaeger@econ.kuleuven.be
6 Overview Revisiting Traditional analytics Improving Traditional analytics Social networks and applications A three-layered social network learner Case study: social networks in Telco Markov assumption Local versus Network variables Featurization Empirical Findings Conclusions 6
7 Revisting Traditional Analytics
8 Traditional Analytics: Performance benchmarks
9 Improving Traditional Analytics: 2 strategies Strategy 1: Use complex modeling techniques E.g. neural networks, support vector machines, random forests, Pro: powerful models (e.g. universal approximation) Con: loss of interpretability, marginal performance gains Strategy 2: Enrich your data External data (FICO score, bureau data, ) Social Network data! Pro: model still interpretable Con: additional resources needed (economic, computational) 9
10 Traditional Approach to Analytics
11 Social Networks: Nodes versus Edges Nodes Customer (private/professional), household/family, patient, doctor, paper, author, terrorist, Web page, Edges Different kinds of relationships, e.g., colleagues, friends, patients, disease, contact, reference, Weighted based on, e.g., interaction frequency, importance of information exchange, intimacy, emotional intensity, 11
12 Example Social Network Applications Churn detection in a Telco setting Nodes are customers Edges are calling patterns between customers (based on CDR data) System risk in a Credit Risk setting Nodes are banks Edges are liquidity dependencies Anti-Money Laundering Nodes are bank accounts Edges are money transfers Viral marketing Nodes are customers Edges are messages 12
13 Social Network Analytics: Challenges Finding the right balance between local, customer specific versus network information It s not all in the network! Need procedures to infer the behavior of all nodes simultaneously Collective inference procedures (e.g. Gibbs sampling) No easy separation in training and test set Cannot just cut the network in two! Out-of-time validation needed 13
14 Out-of-Sample versus Out-of-Time Validation Time 14
15 A three layered Social Network Learner Local model Only uses local (e.g., customer specific) information E.g. socio-demographic, RFM, customer interaction, Can be estimated using e.g. logistic regression, decision trees, Network model Takes into account the network information Collective inference Determines how the nodes mutually influence each other 15
16 16
17 Case Study: Social Networks in Telco Traditional customer churn prediction models treat customers as isolated entities Customers are however believed to be strongly influenced by their social environment Recommendations from peers, mouth-to-mouth publicity Social leader influence Promotions to acquire groups of friends Reduced tariffs for intra-operator traffic 17
18 Local Models for Churn Prediction 18
19 Constructing a social network using CDR Data Call Detail Records (CDR) data Detailed logs about each interaction involving a customer Gigabytes to Terabytes of data each day Extract the call graph using computationally efficient algorithms Represent call graph as sparse matrix Edge definition (SMS/Voice/MMS/ / ) I MOBISTAR MOBILE 99 21JAN2010:23:45: I Base SMSC Platform 99 21JAN2010:23:46: I Proximus SMSC Platform 99 21JAN2010:23:45:
20 From CDR data to Sparse Matrix Need facilities for sparse matrix handling and parallel computing I MOBISTAR MOBILE 99 21JAN2010:23:45: I Base SMSC Platform 99 21JAN2010:23:46: I Proximus SMSC Platform 99 21JAN2010:23:45: Raw CDRs G 2 F 3 B 7 8 C A 9 4 D 3 H 3 Weighted network 2 E J I
21 Case Study: European Telco operator Prepaid segment; about customers 5 months call detail records + local attributes Churn rate 0.5% per month (skewed class distribution!) Weighted edges: number of seconds called during 3 months About edges Total data set about 300 Gigabytes in size
22 The Markov assumption The class/behavior of a node in the network only depends upon the class/behavior of its direct neighbors Aka homophily, guilt by association Birds of a feather, flock together attributed to Robert Burton ( ) (People) love those who are like themselves Aristotle, Rhetoric and Nichomachean Ethics Needed to facilitate computations (cf. Markov chains) 22
23 Local versus Network Variables A network variable aggregates information that is contained within a network structure and makes a differentiation in the destination of outgoing links or the origin of incoming links Examples: the number of contacts (local variable) the number of contacts with churners (network variable) the number of international calls (network variable) 23
24 Local versus Network variables 24
25 A Basic Network Model: Featurization Featurization or propositionalization: translate network into traditional attributes Network attributes can be included in traditional model (e.g. logistic regression) Create as many as possible and do stepwise regression A simple, interpretable social network classifier! 25
26 Example Network Model: Featurization
27 Example Network Model: WVRN
28 Results: Finding 1 Network models boost performance and profit compared to a local model Incremental profit increase compared to no network effects 28
29 Results: Finding 2 Non-Markovian network effects incorporating the impact of higher order neighbors leads to improved predictive power and profit! Incremental profit increase compared to first order network effects Note: higher order effects previously discovered in the spreading of happiness and obesitas (N. Christakis, Social networks and happiness ) 29
30 Results: Finding 3 Network models detect other types of churners compared to traditional models! Fraction of the churners detected by the network models (as a function of the selected fraction of customers, ranked according to their predicted probability to churn), that are NOT detected by the local model Different curves represent different network models (induced by different techniques) Synergy opportunities! 30
31 Ensemble approach : Combining Local and Network models Use two models in parallel by selecting customers indicated by the local model and the network model Decide upon optimal fraction (current research) Local model Network model Ensemble model output 31
32 Ensemble approach: 2D Lift Curve 32
33 Current Research Topics Extensions towards regression context (e.g. CLV) Applications in other contexts (e.g. credit risk, anti-money laundering, customer acquisition, ) Integrating local information in a network learner Quasi-Social Networks Community mining Backtesting 33
34 Key lessons learnt Introduced a three-layer social network learning environment (local information, network information, collective inferencing) Defined local versus network variables Introduced featurization as a basic social network learner Discussed how non-markovian behavior can be modelled in a straightforward way Illustrated the theoretical concepts using a real-life case study about churn prediction in the Telco sector 34
35 References VERBEKE W., DEJAEGER K, MARTENS D., HUR J., BAESENS B., New insights into churn prediction in the telecommunication sector: a profit driven data mining approach, European Journal of Operational Research, forthcoming, DEJAEGER K., VERBEKE W., MARTENS D., BAESENS B., Data Mining Techniques for Software Effort Estimation: a Comparative Study, IEEE Transactions on Software Engineering, forthcoming MARTENS D., FAWCETT T., BAESENS B., Editorial Survey: Swarm Intelligence for Data Mining, Machine Learning, Volume 82, Number 1, pp. 1-42, VERBEKE W., MARTENS D., MUES C., BAESENS B., Building customer churn prediction models with advanced rule induction techniques, Expert Systems with Applications, Volume 38, pp , BAESENS B., MUES C., MARTENS D., VANTHIENEN J., 50 years of Data Mining and OR: upcoming trends and challenges, Journal of the Operational Research Society, Volume 60, pp , GLADY N., CROUX C., BAESENS B., Modeling Churn Using Customer Lifetime Value, European Journal of Operational Research, Volume 197, Number 1, pp , MARTENS D., BAESENS B., VAN GESTEL T., Decompositional Rule Extraction from Support Vector Machines by Active Learning, IEEE Transactions on Knowledge and Data Engineering, Volume 21, Number 1, pp , GLADY N., CROUX C., BAESENS B., A Modified Pareto/NBD Approach for Predicting Customer Lifetime Value, Expert Systems With Applications, Volume 36, Number 2, pp , BAESENS B., SETIONO R., MUES C., VANTHIENEN J., Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation, Management Science, Volume 49, Number 3, pp , March
36 FYI Advanced Analytics for Customer Intelligence Using SAS Lecturer: prof. dr. Bart Baesens 3-day course offered Many companies have gathered huge amounts of customer data about marketing success, use of financial services, online usage, and even fraud behavior. Given recent trends and needs such as mass customization, personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. This course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data-mining techniques for advanced customer intelligence applications. This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. Background material such as selected papers, tutorials, and guidelines are provided. 36
37 Acknowledgments Jerry Oglesby, Director Global Academic Program & Global Certification Education Division Larry Stewart, SAS Education Vice President Sean O Brien, Director, Business and Curriculum Development Bob Lucas, Statistical Training and Technical Services Director Karen Washburn, Business Knowledge Series Manager Patsy Poole, Project Manager Hillary Kokes, former Business Knowledge Series Manager Lieve Goedhuys, former Academic Program Manager, SAS Institute Belgium-Luxembourg All the other great SAS folks for the excellent collaboration during the past years! 37
38 Q & A 38 Copyright 2012, SAS Institute Inc. All rights reserved.
39 Additional Resources Live Classes Advanced Analytics for Customer Intelligence Using SAS Analytics: Putting It All to Work Upcoming Live Webinars May 18: Getting Started with SAS Enterprise Miner June 14: SAS Information Management: Leverage and Extend Hadoop SAS Talks on support.sas.com Upcoming Live Events Analytics 2012 Follow along on Twitter using #sastalks 39 Copyright 2012, SAS Institute Inc. All rights reserved.
40 support.sas.com Copyright 2011, SAS Institute Inc. All rights reserved.
Advanced Analytics Course Series
SAS Education Advanced Analytics Course Series September / October 2015 Discover real value in your corporate data. www.sas.de/education/analytics SAS Education Analytical Concepts Learn How to Explore
More informationBIG DATA IN BANKING AND INSURANCE
BIG DATA IN BANKING AND INSURANCE Prof. dr. Bart Baesens Department of Decision Sciences and Information Management, KU Leuven (Belgium) School of Management, University of Southampton (United Kingdom)
More informationMining Telecommunication Networks to Enhance Customer Lifetime Predictions
Mining Telecommunication Networks to Enhance Customer Lifetime Predictions Aimée Backiel 1, Bart Baesens 1,2, and Gerda Claeskens 1 1 Faculty of Economics and Business, KU Leuven, Belgium {aimee.backiel,bart.baesens,gerda.claeskens}@kuleuven.be
More informationCOPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments
Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for
More informationDIGITS CENTER FOR DIGITAL INNOVATION, TECHNOLOGY, AND STRATEGY THOUGHT LEADERSHIP FOR THE DIGITAL AGE
DIGITS CENTER FOR DIGITAL INNOVATION, TECHNOLOGY, AND STRATEGY THOUGHT LEADERSHIP FOR THE DIGITAL AGE INTRODUCTION RESEARCH IN PRACTICE PAPER SERIES, FALL 2011. BUSINESS INTELLIGENCE AND PREDICTIVE ANALYTICS
More informationBusiness Analytics. Prof. dr. ir. Wouter Verbeke. BEST Summer Course July 8, 2015
Business Analytics Prof. dr. ir. Wouter Verbeke BEST Summer Course July 8, 2015 Wouter Verbeke Assistant professor of business informatics and business analytics at VU Brussels, Belgium, Faculty of Economic
More informationData are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90
FREE echapter C H A P T E R1 Big Data and Analytics Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90 percent of the data in the
More informationData are everywhere. IBM projects that every day we generate 2.5
C HAPTER 1 Big Data and Analytics Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. 1 In relative terms, this means 90 percent of the data in the world has been
More informationNine Common Types of Data Mining Techniques Used in Predictive Analytics
1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better
More informationData Mining Techniques in CRM
Data Mining Techniques in CRM Inside Customer Segmentation Konstantinos Tsiptsis CRM 6- Customer Intelligence Expert, Athens, Greece Antonios Chorianopoulos Data Mining Expert, Athens, Greece WILEY A John
More informationUsing reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management
Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Paper Jean-Louis Amat Abstract One of the main issues of operators
More informationHow To Make A Credit Risk Model For A Bank Account
TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions
More informationApplying Sonamine Social Network Analysis To Telecommunications Marketing. An introductory whitepaper
Applying Sonamine Social Network Analysis To Telecommunications Marketing An introductory whitepaper Introduction Social network analysis (SNA) uses information about the relationships between customers
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationCRM at Ghent University
CRM at Ghent University Customer Relationship Management at Department of Marketing Faculty of Economics and Business Administration Ghent University Prof. Dr. Dirk Van den Poel Updated April 6th, 2005
More informationContents. Preface xiii. Acknowledgments xv
From Analytics in a Big Data World. Full book available for purchase here. Contents Preface xiii Acknowledgments xv Chapter 1 Big Data and Analytics 1 Example Applications 2 Basic Nomenclature 4 Analytics
More informationBanking Analytics Training Program
Training (BAT) is a set of courses and workshops developed by Cognitro Analytics team designed to assist banks in making smarter lending, marketing and credit decisions. Analyze Data, Discover Information,
More informationCustomer Relationship Management
V. Kumar Werner Reinartz Customer Relationship Management Concept, Strategy, and Tools ^J Springer Part I CRM: Conceptual Foundation 1 Strategic Customer Relationship Management Today 3 1.1 Overview 3
More informationMastering Big Data. Steve Hoskin, VP and Chief Architect INFORMATICA MDM. October 2015
Mastering Big Data Steve Hoskin, VP and Chief Architect INFORMATICA MDM October 2015 Agenda About Big Data MDM and Big Data The Importance of Relationships Big Data Use Cases About Big Data Big Data is
More informationANALYTICS IN BIG DATA ERA
ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut
More informationHow Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK
How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK Agenda Analytics why now? The process around data and text mining Case Studies The Value of Information
More informationAdvanced In-Database Analytics
Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??
More informationA STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH
205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology
More informationRole of Social Networking in Marketing using Data Mining
Role of Social Networking in Marketing using Data Mining Mrs. Saroj Junghare Astt. Professor, Department of Computer Science and Application St. Aloysius College, Jabalpur, Madhya Pradesh, India Abstract:
More informationCoolaData Predictive Analytics
CoolaData Predictive Analytics 9 3 6 About CoolaData CoolaData empowers online companies to become proactive and predictive without having to develop, store, manage or monitor data themselves. It is an
More informationEnhanced Boosted Trees Technique for Customer Churn Prediction Model
IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V5 PP 41-45 www.iosrjen.org Enhanced Boosted Trees Technique for Customer Churn Prediction
More informationRevenue Enhancement and Churn Prevention
Revenue Enhancement and Churn Prevention for Telecom Service Providers A Telecom Event Analytics Framework to Enhance Customer Experience and Identify New Revenue Streams www.wipro.com Anindito De Senior
More informationWebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat
Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise
More informationClassification of Bad Accounts in Credit Card Industry
Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition
More informationBig Data in Telecom value chain. Presented by: Gurjot S Sandhu Director Sales Xalted Information Systems Pvt. Ltd.
Big Data in Telecom value chain Presented by: Gurjot S Sandhu Director Sales Xalted Information Systems Pvt. Ltd. Analyzing Big Picture Big Data to CSPs Traditional Analytics vs Big Data Approach Traditional
More informationUnlocking Value from. Patanjali V, Lead Data Scientist, Tiger Analytics Anand B, Director Analytics Consulting,Tiger Analytics
Unlocking Value from Patanjali V, Lead Data Scientist, Anand B, Director Analytics Consulting, EXECUTIVE SUMMARY Today a lot of unstructured data is being generated in the form of text, images, videos
More informationDORMANCY PREDICTION MODEL IN A PREPAID PREDOMINANT MOBILE MARKET : A CUSTOMER VALUE MANAGEMENT APPROACH
DORMANCY PREDICTION MODEL IN A PREPAID PREDOMINANT MOBILE MARKET : A CUSTOMER VALUE MANAGEMENT APPROACH Adeolu O. Dairo and Temitope Akinwumi Customer Value Management Department, Segments and Strategy
More informationThe Data Mining Process
Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data
More informationCHURN PREDICTION IN MOBILE TELECOM SYSTEM USING DATA MINING TECHNIQUES
International Journal of Scientific and Research Publications, Volume 4, Issue 4, April 2014 1 CHURN PREDICTION IN MOBILE TELECOM SYSTEM USING DATA MINING TECHNIQUES DR. M.BALASUBRAMANIAN *, M.SELVARANI
More informationNetwork Interactions in Mobile Networks
Predicting Consumer Choices Through Analysis of Interactions in Social Networks Todor Krastevich * Summary: Analysis of interactions in social networks has emerged as a new research paradigm in modern
More informationData Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved.
Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA
More informationData Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms
Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge
More informationJournée Thématique Big Data 13/03/2015
Journée Thématique Big Data 13/03/2015 1 Agenda About Flaminem What Do We Want To Predict? What Is The Machine Learning Theory Behind It? How Does It Work In Practice? What Is Happening When Data Gets
More informationHow to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning
How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume
More informationMapReduce Approach to Collective Classification for Networks
MapReduce Approach to Collective Classification for Networks Wojciech Indyk 1, Tomasz Kajdanowicz 1, Przemyslaw Kazienko 1, and Slawomir Plamowski 1 Wroclaw University of Technology, Wroclaw, Poland Faculty
More informationDATA MINING TECHNIQUES AND APPLICATIONS
DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,
More informationThe Real Benefits from Text Mining
The Real Benefits from Text Mining Olivier Jouve Vice President SPSS Rebecca Wettemann Vice President Nucleus Research Agenda SPSS and Text Mining Our analysis of text mining Identifying the biggest benefits
More informationKnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES
HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES Translating data into business value requires the right data mining and modeling techniques which uncover important patterns within
More informationMaximize Revenues on your Customer Loyalty Program using Predictive Analytics
Maximize Revenues on your Customer Loyalty Program using Predictive Analytics 27 th Feb 14 Free Webinar by Before we begin... www Q & A? Your Speakers @parikh_shachi Technical Analyst @tatvic Loves js
More informationMobile Phone APP Software Browsing Behavior using Clustering Analysis
Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Mobile Phone APP Software Browsing Behavior using Clustering Analysis
More informationHexaware E-book on Predictive Analytics
Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,
More informationAdvanced Database Marketing Innovative Methodologies and Applications for Managing Customer Relationships
Advanced Database Marketing Innovative Methodologies and Applications for Managing Customer Relationships Edited by KRISTOF COUSSEMENT KOEN W. DE BOCK and SCOTT A. NESLIN GOWER Contents List of Figures
More informationPromises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Spring 2015 Thomas Hill, Ph.D. VP Analytic Solutions Dell Statistica Overview and Agenda Dell Software overview Dell in
More informationData-Driven Decisions: Role of Operations Research in Business Analytics
Data-Driven Decisions: Role of Operations Research in Business Analytics Dr. Radhika Kulkarni Vice President, Advanced Analytics R&D SAS Institute April 11, 2011 Welcome to the World of Analytics! Lessons
More informationnot possible or was possible at a high cost for collecting the data.
Data Mining and Knowledge Discovery Generating knowledge from data Knowledge Discovery Data Mining White Paper Organizations collect a vast amount of data in the process of carrying out their day-to-day
More informationSTATISTICA. Financial Institutions. Case Study: Credit Scoring. and
Financial Institutions and STATISTICA Case Study: Credit Scoring STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table of Contents INTRODUCTION: WHAT
More informationWhat is Data Science? Data, Databases, and the Extraction of Knowledge Renée T., @becomingdatasci, November 2014
What is Data Science? { Data, Databases, and the Extraction of Knowledge Renée T., @becomingdatasci, November 2014 Let s start with: What is Data? http://upload.wikimedia.org/wikipedia/commons/f/f0/darpa
More informationMS1b Statistical Data Mining
MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to
More informationData Mining & Data Stream Mining Open Source Tools
Data Mining & Data Stream Mining Open Source Tools Darshana Parikh, Priyanka Tirkha Student M.Tech, Dept. of CSE, Sri Balaji College Of Engg. & Tech, Jaipur, Rajasthan, India Assistant Professor, Dept.
More informationInsurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.
Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics
More informationWhite Paper. How Streaming Data Analytics Enables Real-Time Decisions
White Paper How Streaming Data Analytics Enables Real-Time Decisions Contents Introduction... 1 What Is Streaming Analytics?... 1 How Does SAS Event Stream Processing Work?... 2 Overview...2 Event Stream
More informationGraph Mining and Social Network Analysis
Graph Mining and Social Network Analysis Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
More informationLeveraging Ensemble Models in SAS Enterprise Miner
ABSTRACT Paper SAS133-2014 Leveraging Ensemble Models in SAS Enterprise Miner Miguel Maldonado, Jared Dean, Wendy Czika, and Susan Haller SAS Institute Inc. Ensemble models combine two or more models to
More informationANALYTICS IN BIG DATA ERA
ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut
More informationMachine Learning and Data Mining. Fundamentals, robotics, recognition
Machine Learning and Data Mining Fundamentals, robotics, recognition Machine Learning, Data Mining, Knowledge Discovery in Data Bases Their mutual relations Data Mining, Knowledge Discovery in Databases,
More informationData Mining - Evaluation of Classifiers
Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010
More informationSunnie Chung. Cleveland State University
Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:
More informationON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION
ISSN 9 X INFORMATION TECHNOLOGY AND CONTROL, 00, Vol., No.A ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION Danuta Zakrzewska Institute of Computer Science, Technical
More informationInformation Systems Roles in the Value Chain Customer Relationship Management (CRM) Systems 09/11/2015. ACS 3907 E-Commerce
ACS 3907 E-Commerce Instructor: Kerry Augustine November 10 th 2015 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEMS Managing materials, services and information from suppliers through to the organization
More informationACS 3907 E-Commerce. Instructor: Kerry Augustine November 10 th 2015. Bowen Hui, Beyond the Cube Consulting Services Ltd.
ACS 3907 E-Commerce Instructor: Kerry Augustine November 10 th 2015 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEMS Managing materials, services and information from suppliers through to the organization
More information2015 Workshops for Professors
SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market
More informationData Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
More informationAn Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
More informationSOCIAL NETWORK ANALYSIS EVALUATING THE CUSTOMER S INFLUENCE FACTOR OVER BUSINESS EVENTS
SOCIAL NETWORK ANALYSIS EVALUATING THE CUSTOMER S INFLUENCE FACTOR OVER BUSINESS EVENTS Carlos Andre Reis Pinheiro 1 and Markus Helfert 2 1 School of Computing, Dublin City University, Dublin, Ireland
More informationInferential Statistics. Data Mining. ASC September Proving value in complex analytics. 2 Rivers. Information and Data Management
ASC September Proving value in complex analytics 26 th September 2014 John McConnell Information and Data Management 1 1 2 Rivers Research Operational/Transactional Inferential Statistics Inferring parameter
More informationAre You Ready for Big Data?
Are You Ready for Big Data? Jim Gallo National Director, Business Analytics February 11, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?
More informationNetwork Analytics in Marketing
Network Analytics in Marketing Prof. Dr. Daning Hu Department of Informatics University of Zurich Nov 13th, 2014 Introduction: Network Analytics in Marketing Marketing channels and business networks have
More informationBIG DATA What it is and how to use?
BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14
More informationAre You Ready for Big Data?
Are You Ready for Big Data? Jim Gallo National Director, Business Analytics April 10, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?
More informationData Science and Business Analytics Certificate Data Science and Business Intelligence Certificate
Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate Description The Helzberg School of Management has launched two graduate-level certificates: one in Data
More informationCollective Behavior Prediction in Social Media. Lei Tang Data Mining & Machine Learning Group Arizona State University
Collective Behavior Prediction in Social Media Lei Tang Data Mining & Machine Learning Group Arizona State University Social Media Landscape Social Network Content Sharing Social Media Blogs Wiki Forum
More informationImprove Marketing Campaign ROI using Uplift Modeling. Ryan Zhao http://www.analyticsresourcing.com
Improve Marketing Campaign ROI using Uplift Modeling Ryan Zhao http://www.analyticsresourcing.com Objective To introduce how uplift model improve ROI To explore advanced modeling techniques for uplift
More informationPredicting & Preventing Banking Customer Churn by Unlocking Big Data
Predicting & Preventing Banking Customer Churn by Unlocking Big Data Customer Churn: A Key Performance Indicator for Banks In 2012, 50% of customers, globally, either changed their banks or were planning
More informationPolitecnico di Torino. Porto Institutional Repository
Politecnico di Torino Porto Institutional Repository [Proceeding] NEMICO: Mining network data through cloud-based data mining techniques Original Citation: Baralis E.; Cagliero L.; Cerquitelli T.; Chiusano
More informationFinding Minimal Neural Networks for Business Intelligence Applications
Finding Minimal Neural Networks for Business Intelligence Applications Rudy Setiono School of Computing National University of Singapore www.comp.nus.edu.sg/~rudys d Outline Introduction Feed-forward neural
More informationData Mining Algorithms Part 1. Dejan Sarka
Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses
More informationMachine Learning for Display Advertising
Machine Learning for Display Advertising The Idea: Social Targeting for Online Advertising Performance Current ad spending seems disproportionate Online Advertising Spending Breakdown (2009) Source: IAB
More informationEasily Identify Your Best Customers
IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do
More informationData Analytical Framework for Customer Centric Solutions
Data Analytical Framework for Customer Centric Solutions Customer Savviness Index Low Medium High Data Management Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014
RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer
More informationBIG DATA STRATEGY. Rama Kattunga Chair at American institute of Big Data Professionals. Building Big Data Strategy For Your Organization
BIG DATA STRATEGY Rama Kattunga Chair at American institute of Big Data Professionals Building Big Data Strategy For Your Organization In this session What is Big Data? Prepare your organization Building
More informationCustomer Sensitivity to Credit Risk Decisions
Customer Sensitivity to Credit Risk Decisions Matthew O Kane Senior Manager Accenture Analytics August 213 What is an Individual Decision Effect? Alternative A Function Churn Marketing Risk decisioning
More informationData Mining Solutions for the Business Environment
Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over
More informationANALYTICS CENTER LEARNING PROGRAM
Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals
More informationA Comparative Assessment of the Performance of Ensemble Learning in Customer Churn Prediction
The International Arab Journal of Information Technology, Vol. 11, No. 6, November 2014 599 A Comparative Assessment of the Performance of Ensemble Learning in Customer Churn Prediction Hossein Abbasimehr,
More informationChapter 7: Data Mining
Chapter 7: Data Mining Overview Topics discussed: The Need for Data Mining and Business Value The Data Mining Process: Define Business Objectives Get Raw Data Identify Relevant Predictive Variables Gain
More informationBuilding and Deploying Customer Behavior Models
Building and Deploying Customer Behavior Models February 20, 2014 David Smith, VP Marketing and Community, Revolution Analytics Paul Maiste, President and CEO, Lityx In Today s Webinar About Revolution
More informationData Mining + Business Intelligence. Integration, Design and Implementation
Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution
More informationTransforming the Telecoms Business using Big Data and Analytics
Transforming the Telecoms Business using Big Data and Analytics Event: ICT Forum for HR Professionals Venue: Meikles Hotel, Harare, Zimbabwe Date: 19 th 21 st August 2015 AFRALTI 1 Objectives Describe
More informationWhy include analytics as part of the School of Information Technology curriculum?
Why include analytics as part of the School of Information Technology curriculum? Lee Foon Yee, Senior Lecturer School of Information Technology, Nanyang Polytechnic Agenda Background Introduction Initiation
More informationHadoop s Advantages for! Machine! Learning and. Predictive! Analytics. Webinar will begin shortly. Presented by Hortonworks & Zementis
Webinar will begin shortly Hadoop s Advantages for Machine Learning and Predictive Analytics Presented by Hortonworks & Zementis September 10, 2014 Copyright 2014 Zementis, Inc. All rights reserved. 2
More informationAn Overview of Predictive Analytics for Practitioners. Dean Abbott, Abbott Analytics
An Overview of Predictive Analytics for Practitioners Dean Abbott, Abbott Analytics Thank You Sponsors Empower users with new insights through familiar tools while balancing the need for IT to monitor
More informationExecutive Briefing White Paper Plant Performance Predictive Analytics
Executive Briefing White Paper Plant Performance Predictive Analytics A Data Mining Based Approach Abstract The data mining buzzword has been floating around the process industries offices and control
More information