Data Mining

  • 作   者:

    Nitin Patel

  • 出版社:

    Foreign Language Teaching and Research Press

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Data mining is gaining momentum these days. Through this course, you are able to develop an understanding of the strengths and limitations of popular data mining techniques and to be able to identify promising business applications of data mining. Students will be able to actively manage and participate in data mining projects executed by consultants or specialists in data mining. A useful takeaway from the course will be the ability to perform powerful data analysis in Excel.


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. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to-use software and cases.


Nitin Patel is a recognized expert on the development of fast and accurate computer algorithms to implement computationally intensive statistical methods. He has published over sixty-five refereed papers in the areas of statistics, operations research and computing and co-authored a book on data mining for end-users. Dr. Patel is a Fellow of the American Statistical Association. Along with Dr. Cyrus Mehta and Dr. Karim Hirji, he received the 1987 George W.Snedecor Award from the American Statistical Association. Dr. Patel has been a visiting professor at MIT since 1995. Previously, he was CMC chair professor at the Indian Institute of Management, and held visiting positions at Harvard, the University of Michigan, the University of Montreal and the University of Pittsburgh. He was a co-founder of Tata Consultancy Services, a leading Indian software company and is a Fellow of the Computer Society of India.


Recently coined term for confluence of ideas from statistics and computer science (machine learning and database methods) applied to large databases in science, engineering and business.

  • 第一课 数据挖掘概述

  • 第二课 k-最近邻算法在分类和预测中的应用

  • 第三课 分类器性能评价

  • 第四课 分类树

  • 第五课 判别分析

  • 第六课 Logistic的回归

  • 第七课 手摇纺织机Saris的销售

  • 第八课 神经网络

  • 第九课 多元线性回归概述

  • 第十课 数据挖掘中的多元线性回归

  • 第十一课 数据挖掘技术比较

  • 第十二课 聚类分析

  • 第十三课 降维:主成分分析

  • 第十四讲 在交易数据库中发现关联规则

  • Lecture 1 Data Mining Overview

  • Lecture 2 k-Nearest Neighbor Algorithms for Classification and Prediction

  • Lecture 3 Judging the Performance of Classifiers

  • Lecture 4 Classification Trees

  • Lecture 5 Discriminant Analysis

  • Lecture 6 Logistic Regression

  • Lecture 7 Sales of Handloom Saris

  • Lecture 8 Neural Nets

  • Lecture 9 Multiple Linear Regression Review

  • Lecture 10 Multiple Linear Regression in Data Mining

  • Lecture 11 Comparison of Data Mining Techniques

  • Lecture 12 Cluster Analysis

  • Lecture 13 Dimensionality Reduction: Principal Components Analysis

  • Lecture 14 Discovering Association Rules in Transaction Databases