3.
TABLE OF CONTENTS
Introduction
Data mining: In brief ................................................................... 1
Data mining: What it can’t do ..................................................... 1
Data mining and data warehousing ............................................. 2
Data mining and OLAP ............................................................... 3
Data mining, machine learning and statistics .............................. 4
Data mining and hardware/software trends................................. 4
Data mining applications ............................................................. 5
Successful data mining ................................................................ 5
Data Description for Data Mining
Summaries and visualization ....................................................... 6
Clustering .................................................................................... 6
Link analysis ............................................................................... 7
Predictive Data Mining
A hierarchy of choices................................................................. 9
Some terminology ..................................................................... 10
Classification ............................................................................. 10
Regression ................................................................................. 10
Time series ................................................................................ 10
Data Mining Models and Algorithms
Neural networks ........................................................................ 11
Decision trees ............................................................................ 14
Multivariate Adaptive Regression Splines (MARS) ................. 17
Rule induction ........................................................................... 17
K-nearest neighbor and memory-based reasoning (MBR) ....... 18
Logistic regression .................................................................... 19
Discriminant analysis ................................................................ 19
Generalized Additive Models (GAM) ....................................... 20
Boosting .................................................................................... 20
Genetic algorithms .................................................................... 21
The Data Mining Process
Process Models ......................................................................... 22
The Two Crows Process Model ................................................ 22
Selecting Data Mining Products
Categories .................................................................................. 34
Basic capabilities ....................................................................... 34
Summary ............................................................................................ 36
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