Principles of data mining

✍ Scribed by Max Bramer

• Publisher: Springer
• Year: 2007
• Tongue: English
• Leaves: 340
• Series: Undergraduate topics in computer science
• Edition: 1
• Category: Library

✦ Synopsis

This is an undergraduate introduction to data mining. The book doesn't go into details. It may be suitable for people who want to get a quick feel of the data mining field. People who need more details shall read more serious and comprehensive introductions. Overall I am giving 4 stars, because I liked it.

✦ Table of Contents

Cover......Page 1
Principles of Data Mining......Page 2
Contents......Page 4
Introduction to Data Mining......Page 10
1 Data for Data Mining......Page 19
2 Introduction to Classification: Naive Bayes and Nearest Neighbour......Page 30
3 Using Decision Trees for Classification......Page 47
4 Decision Tree Induction: Using Entropy for Attribute Selection......Page 57
5 Decision Tree Induction: Using Frequency Tables for Attribute Selection......Page 71
6 Estimating the Predictive Accuracy of a Classifier......Page 84
7 Continuous Attributes......Page 98
8 Avoiding Overfitting of Decision Trees......Page 124
9 More About Entropy......Page 140
10 Inducing Modular Rules for Classification......Page 160
11 Measuring the Performance of a Classifier......Page 177
12 Association Rule Mining I......Page 190
13 Association Rule Mining II......Page 205
14 Clustering......Page 222
15 Text Mining......Page 240
References......Page 255
A.1 Subscript Notation......Page 257
A.2 Trees......Page 260
A.3 The Logarithm Function log2X......Page 264
A.4 Introduction to Set Theory......Page 267
B Datasets......Page 272
C Sources of Further Information......Page 292
D Glossary and Notation......Page 295
E Solutions to Self-assessment Exercises......Page 312
Index......Page 336