ECT584
 Spring 2015

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Schedule and Class Material


Week 1 - March 30
Topics Class Material & Resources Assignments/Readings
*  Introduction to the Course
*  Overview of Data Mining and Web Data Mining
*  About this Course
*  Overview of Data Mining and Knowledge Discovery Process
*  Overview of Web Mining Part I
*  Overview of Web Mining Part II
*  Post an Introductory Message on the Getting Acquainted Board on the D2L Page for the Course
*  Read Chapter 1 of Berry and Linoff
*  Read Wikipedia entries for Web Analytics and Web Mining
*  Watch this interview with Prof. Bamshad Mobasher on Data Mining, security, and Privacy in a recent episode of the "Public Perspective" program which airs in the Chicago area.
Week 2 - April 6
Topics Class Material & Resources Assignments/Readings
*  Understanding, Preparing, and Exploring Data
*  Overview of WEKA Data Mining Package





*  Understanding Characteristics of Data
*  Data Preparation and Preprocessing
*  Video: Preprocessing with WEKA (31 min)
*  Read Chapters 3 and 4 of Berry and Linoff
*  Read Driving e-Commerce Profitability From Online and Offline Data, White paper form Torrent Systems
*  WEKA has a MOOC. View lectures 1.2 and 1.3 for a good introduction to the WEKA package.
Week 3 - April 13
Topics Class Material & Resources Assignments/Readings
*  Mining Frequent Patterns



*  Market Basket Analysis & Association Rule Mining
*  Mining Sequential & Navigational Patterns
* Video: Mining Association Rules Using WEKA (23 min)
*  Read Chapter 15 of Berry and Linoff
*  Read Chapter 2 of B. Liu's Book (Association Rules & Sequential Patterns)
Week 4 - April 20
Topics Class Material & Resources Assignments/Readings
*  Predictive Modeling Concepts, Algorithms, and Applications




*  Basic Concepts in Classification & Prediction
*  Decision Tree Classification
*  Bayesian Classification
*  Read Chapters 5 and 7 of Berry and Linoff
*  Read Chapter 3 of B. Liu's Book (Supervised Learning)
*  Building Classification Models: ID3 and C4.5 - from the AI course at Temple university.
Week 5 - April 27
Topics Class Material & Resources Assignments/Readings
*  Predictive Modeling Concepts, Algorithms, and Applications (Cont.)
*  Distance-Based Classification and Prediction
*  Recommender Systems




*  Distances, Similarities, and Predictive Modeling using K-Nearest-Neighbors
*  Applications of Predictive Modeling in Recommender Systems
*  Read Chapter 9 of Berry and Linoff
*  Read Recommender Systems Article in the Encyclopedia of Machine Learning
*  Read Wikipedia article on Collaborative Filtering
Week 6 -  May 4
Topics Class Material & Resources Assignments/Readings
*  Predictive Modeling Concepts, Algorithms, and Applications (Cont.)





*  Classification & Prediction using WEKA
*  View lectures 3.3, 3.4, 3.5 and 3.6 if the WEKA  MOOC for a good introduction to several classification approaches using WEKA.
   
Week 7 -  May 11
Topics Class Material & Resources Assignments/Readings
*  Finding Groups and Similarities  In Data





*  Basic Clustering Concepts & Algorithms
* Clustering Applications in Web Mining, User Profiling, and Personalization
*  Kmeans Clustering with WEKA
*  Read Chapters 13 and 14 of Berry and Linoff
*  Read Chapter 4 of B. Liu's Book (Unsupervised Learning)
*  Review Wikipedia pages on Cluster Analysis, including the articles on Kmeans Clustering and Hierarchical Clustering.
Week 8 - May 18
Topics Class Material & Resources Assignments/Readings
*  Analytics for E-Commerce and Web Marketing





*  Data Preparation for Web Usage Analytics
   
*  Read Web Usage Mining by B. Mobasher (Ch.12. in B. Liu's Book on Web Mining)
*  Read Chapter 18 of Berry and Linoff
Week 9 - May 25
Topics Class Material & Resources Assignments/Readings
*  Analytics for E-Commerce and Web Marketing (cont.)





*  Web Usage mining for E-Business Analytics

*  Read E-Commerce Intelligence: Measuring, Analyzing, and Reporting on Merchandising Effectiveness of Online Stores, by Stephen Gomory, et. al., IBM T. J. Watson Research Center.
*  Read Lessons and Challenges from Mining Retail E-Commerce Data, by Ron Kohavi, et al., Journal of Machine Learning.
Week 10 - June 1
Topics Class Material & Resources Assignments/Readings
*  E-Business Analytics (cont.)
*  [Supplemental] More on Personalization & Recommendation





*  E-Business Analytics - Case Studies
*  Supplemental Notes on Recommender Systems

*  Review the Final Project Checklist.
*  Matrix Factorization Techniques for Recommender Systems, Y. Koren et al., IEEE Computer, 2009
*  Content-Based Recommender Systems: State of the Art and Trends, P. Lops, et al., 2011
Final Projects Due - Monday, June 8, 11:59 PM
Copyright © 2014-2015, Bamshad Mobasher, DePaul University.