15.062J 数据挖掘：发现创造价值的模型与预测

MIT Course 15 · Operations Research/Statistics · 课号 15.062J

TL;DR

• 数据挖掘从海量商业数据中提取高价值模式，覆盖有监督分类回归与无监督聚类。
• 决策树（CART）、逻辑回归与最近邻算法（KNN）构成客户流失与信用评估的基石。
• 关联规则挖掘与主成分分析（PCA）为商品推荐与市场细分提供低维特征洞察。

课程简介 / Description

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

讲义 / Lecture Notes

• 1
  • Data Mining Overview (PDF)
  • Prediction and Classification with k-Nearest Neighbors
  • Example 1: Riding Mowers (PDF)
  • Table 11.1 from page 584 of: Johnson, Richard, and Dean Wichern.
  • Applied Multivariate Statistical Analysis
  • . 5th ed. Prentice-Hall, 2002. ISBN: 0-13-092553-5.
• 2
  • Classification and Bayes Rule, Naïve Bayes (PDF)
• 3
  • Classification Trees (PDF)
  • "Housing Database (Boston)." Publicly available data at University of California, Irvine School of Information and Computer Science,
  • Machine Learning Repository of Databases
  • .
• 4
  • Discriminant Analysis Example 2: Fisher's Iris data (PDF)
  • "Iris Plant Database." Publicly available data at University of California, Irvine School of Information and Computer Science,
  • Machine Learning Repository of Databases
  • .
• 5
  • Logistic Regression Case (PDF)
  • Handlooms (PDF)
• 6
  • Neural Nets (PDF)
• 7
  • Discussion of homework - see Problem 1 in
  • assignments
  • section
• 8
  • Multiple Regression Review (PDF)
• 9
  • Multiple Linear Regression in Data Mining (PDF)
• 10
  • Regression Trees, Case: IBM/GM weekly returns
  • Comparison of Data Mining Techniques (PDF)
  • Discussion of homework - see Problem 2 in
  • assignments
  • section
• 11
  • k-Means Clustering, Hierarchical Clustering (PDF)
• 12
  • Case: Retail Merchandising
• 13
  • Midterm Exam
• 14
  • Principal Components (PDF)
  • Example 1, Head Measurements of Adult Sons: Rencher, Alvin.
  • Methods of Multivariate Analysis
  • . 2nd ed. Wiley-Interscience, 2002. Table 3.7, p. 79. ISBN: 0-471-46172-5.
  • Example 2, Charactersitics of Wine: "Wine Recognition Database." Publicly available data at University of California, Irvine School of Information and Computer Science,
  • Machine Learning Repository of Databases
  • .
• 15
  • Guest Lecture by Dr. Ira Haimowitz: Data Mining and CRM at Pfizer
• 16
  • Association Rules (Market Basket Analysis) (PDF)
  • Han, Jiawei, and Micheline Kamber.
  • Data Mining: Concepts and Techniques.
  • Morgan Kauffman Publishers, 2001. Example 6.1 (Figure 6.2). ISBN: 1-55860-489-8.
• 17
  • Recommendation Systems: Collaborative Filtering
• 18
  • Guest Lecture by Dr. John Elder IV, Elder Research: The Practice of Data Mining

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