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Clustering for Data Mining

A Data Recovery Approach

Clustering for Data Mining by Boris Mirkin
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INTRODUCTION: HISTORICAL REMARKS

WHAT IS CLUSTERING
Exemplary Problems
Bird's Eye View

WHAT IS DATA
Feature Characteristics
Bivariate Analysis
Feature Space and Data Scatter
Preprocessing and Standardizing Mixed Data

K-MEANS CLUSTERING
Conventional K-Means
Initialization of K-Means
Intelligent K-Means
Interpretation Aids
Overall Assessment

WARD HIERARCHICAL CLUSTERING
Agglomeration: Ward Algorithm
Divisive Clustering with Ward Criterion
Conceptual Clustering
Extensions of Ward Clustering
Overall Assessment

DATA RECOVERY MODELS
Statistics Modeling as Data Recovery
Data Recovery Model for K-Means
Data Recovery Models for Ward Criterion
Extensions to Other Data Types
One-by-One Clustering
Overall Assessment

DIFFERENT CLUSTERING APPROACHES
Extensions of K-Means Clustering
Graph-Theoretic Approaches
Conceptual Description of Clusters
Overall Assessment

GENERAL ISSUES
Feature Selection and Extraction
Data Pre-Processing and Standardization
Similarity on Subsets and Partitions
Dealing with Missing Data
Validity and Reliability
Overall Assessment

CONCLUSION: Data Recovery Approach in Clustering

BIBLIOGRAPHY

Each chapter also contains a section of Base Words
CRC Press; Read online
Title: Clustering for Data Mining
Author: Boris Mirkin
 
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