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Index
Cover
Title
Copyright
Dedication
Contents
Preface
Chapter 1: Introduction to Cluster Analysis
1.1 What Is a Cluster?
1.2 Capturing the Clusters
1.3 Need for Visualizing Data
1.4 The Proximity Matrix
1.5 Dendrograms
1.6 Summary
1.7 Exercises
Chapter 2: Overview of Data Mining
2.1 What Is Data Mining?
2.2 Data Mining Relationship to Knowledge Discovery in Databases
2.3 The Data Mining Process
2.4 Databases and Data Warehousing
2.5 Exploratory Data Analysis and Visualization
2.6 Data Mining Algorithms
2.7 Modeling for Data Mining
2.8 Summary
2.9 Exercises
Chapter 3: Hierarchical Clustering
3.1 Introduction
3.2 Single-Link versus Complete-Link Clustering
3.3 Agglomerative versus Divisive Clustering
3.4 Ward’s Method
3.5 Graphical Algorithms for Single-Link versus Complete-Link Clustering
3.6 Summary
3.7 Exercises
Chapter 4: Partition Clustering
4.1 Introduction
4.2 Iterative Partition Clustering Method
4.3 The Initial Partition
4.4 The Search for Poor Fits
4.5 K-Means Algorithm
4.5.1 MacQueen’s Method
4.5.2 Forgy’s Method
4.5.3 Jancey’s Method
4.6 Grouping Criteria
4.7 BIRCH, a Hybrid Method
4.8 Summary
4.9 Exercises
Chapter 5: Judgmental Analysis
5.1 Introduction
5.2 Judgmental Analysis Algorithm
5.2.1 Capturing R2
5.2.2 Grouping to Optimize Judges’ R2
5.2.3 Alternative Method for JAN
5.3 Judgmental Analysis in Research
5.4 Example JAN Study
5.4.1 Statement of Problem
5.4.2 Predictor Variables
5.4.3 Criterion Variables
5.4.4 Questions Asked
5.4.5 Method Used for Organizing Data
5.4.6 Subjects Judged
5.4.7 Judges
5.4.8 Strategy Used for Obtaining Data
5.4.9 Checking the Model
5.4.10 Extract the Equation
5.5 Summary
5.6 Exercises
Chapter 6: Fuzzy Clustering Models and Applications
6.1 Introduction
6.2 The Membership Function
6.3 Initial Configuration
6.4 Merging of Clusters
6.5 Fundamentals of Fuzzy Clustering
6.6 Fuzzy C-Means Clustering
6.7 Induced Fuzziness
6.8 Summary
6.9 Exercises
Chapter 7: Classification and Association Rules
7.1 Introduction
7.2 Defining Classification
7.3 Decision Trees
7.4 ID3 Tree Construction Algorithm
7.4.1 Choosing the “Best” Feature
7.4.2 Information Gain Algorithm
7.4.3 Tree Pruning
7.5 Bayesian Classification
7.6 Association Rules
7.7 Pruning
7.8 Extraction of Association Rules
7.9 Summary
7.10 Exercises
Chapter 8: Cluster Validity
8.1 Introduction
8.2 Statistical Tests
8.3 Monte Carlo Analysis
8.4 Indices of Cluster Validity
8.5 Summary
8.6 Exercises
Chapter 9: Clustering Categorical Data
9.1 Introduction
9.2 ROCK
9.3 STIRR
9.4 CACTUS
9.5 CLICK
9.6 Summary
9.7 Exercises
Chapter 10: Mining Outliers
10.1 Introduction
10.2 Outlier Detection Methods
10.3 Statistical Approaches
10.4 Outlier Detection by Clustering
10.5 Fuzzy Clustering Outlier Detection
10.6 Summary
10.7 Exercises
Chapter 11: Model-based Clustering
11.1 Introduction
11.2 COBWEB: A Statistical and AI Approach
11.3 Mixture Model for Clustering
11.4 Farley and Raftery Gaussian Mixture Model
11.5 Estimate the Number of Clusters
11.6 Summary
11.7 Exercises
Chapter 12: General Issues
12.1 Introduction
12.2 Data Cleansing
12.3 Which Proximity Measure Should Be Used?
12.4 Identifying and Correcting Outliers
12.5 Further Study Recommendations
12.6 Introduction to Neural Networks
12.7 Interpretation of the Results
12.8 Clustering “Correctness”?
12.9 Topical Research Exercises
Index
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