EMTM 554: Data Mining Syllabus

Before Class

• Homework 1
  • Make sure you have a copy of JMP from the Statistics course. If you do not, or need a version for a macintosh or have other problems, please contact the EMTM office so they can have it ready for you on the first program weekend.

Lecture 1: Overview of Data Mining

• What is Data Mining, and what is it used for?
  • Examples, including your experience
• Data Warehousing
  • OLTP, OLAP, ETL, etc.
• Homework 1
• Readings
  • Text: Chapt 1 Introduction
  • Mastering data mining - Chapts 1 and 2 Data mining in context, Why master the art? (Berry and Linoff)
  • The Long Tail (Chris Anderson)
  • Text: Chapt 3 Data Warehouse and OLAP Technology
  • Supplemental: Discovering Knowledge in Data - Chapt 1 (Larose)
• WebCafe: introDBM.ppt, DataWarehousing.ppt

Lecture 2: Methods

• Visualization: PTDD
• Personalization: collaborative filtering
• Market segmentation: clustering
• Prediction: Decision trees and regression methods
• JMP for Data Mining (The good, the bad, and the ugly)
• Homework 2
• Readings
  • Text: Chapter 6, sections 1,2,3,6,7,9,11 - Classification and Regression
  • Text: Chapter 7.4.1 k-means - clustering
  • Supplemental: Information Visualization in Data Mining and Knowledge Discovery Chapt 2 (Color Plates are separate)
  • Supplemental: Discovering Knowledge in Data (Larose) chapt 6 & 7 Decision trees, Neural networks
• WebCafe: methods.ppt

Lecture 3: Evaluation

• Evaluation: prediction and pitfalls
• Gazelle.com: correlation and causality
• Homework 3
• Readings
  • Text: Chapter 6, sections 12,13 - Accuracy and Error Measures
  • Supplemental: gazelle.com Case Study
• WebCafe: evaluation.ppt, gazelle.ppt

Lecture 4: The DBM process & tools; CRM

• The DBM process, CRISP-DM
• DBM Tools and Industries, vertical and horizontal
• Homework 4
• Readings
  • Mastering data Mining (Berry and Linoff) Chapt. 3 Data Mining Methodology: The Virtuous Cycle Revisited
  • Supplemental: Knowing What to Sell, When, and to Whom (Kumar, Venkatesan and Reinartz)
  • Supplemental: The Dark Side of Customer Analytics (Davenport and Harris)
  • Supplemental: Berry and Linhoff on churn
  • WebCafe: process.ppt, tools.ppt, costing.ppt

Lecture 5: Text mining

• Text mining: IR and IE, easy and hard
• Homework 5
• Readings
  • Text: 10.4 Text Mining
  • Text Mining (Weiss et al.) - Chapt 1
  • Supplemental Text Mining (Weiss et al.) - Chapt 7
  • Supplemental Text: 10.5 web mining
• WebCafe: search.ppt, textmining.ppt

Lecture 6: Strategic Marketing; Summary and Quiz

• Strategic marketing (CapitalOne)
• Course summary
  • special topics (by request)
  • maybe: advanced methods: SVMs, belief nets
  • future directions for KDD
• Project presentations
• Course evaluation
• Quiz
• Homework: final project
• Readings
  • CaptialOne.pdf
• WebCafe: strategy.ppt, summary.ppt

return home