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Often considered more of an art than a science, books on clustering have been dominated by learning through example with techniques chosen almost through trial and error. Even the two most popular, and most related, clustering methods-K-Means for partitioning and Ward´s method for hierarchical clustering-have lacked the theoretical underpinning required to establish a firm relationship between the two methods and relevant interpretation aids. Other approaches, such as spectral clustering or consensus clustering, are considered absolutely unrelated to each other or to the two above mentioned methods.

Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most popular clustering methods: the K-Means and hierarchical techniques, especially for divisive clustering. It significantly expands coverage of the mathematics of data recovery, and includes a new chapter covering more recent popular network clustering approaches-spectral, modularity and uniform, additive, and consensus-treated within the same data recovery approach. Another added chapter covers cluster validation and interpretation, including recent developments for ontology-driven interpretation of clusters. Altogether, the insertions added a hundred pages to the book, even in spite of the fact that fragments unrelated to the main topics were removed.

Illustrated using a set of small real-world datasets and more than a hundred examples, the book is oriented towards students, practitioners, and theoreticians of cluster analysis. Covering topics that are beyond the scope of most texts, the author's explanations of data recovery methods, theory-based advice, pre- and post-processing issues and his clear, practical instructions for real-world data mining make this book ideally suited for teaching, self-study, and professional reference.



Table of Contents
What Is Clustering

Key Concepts

Case Study Problems

Bird's-Eye View


What Is Data

Key Concepts

Feature Characteristics

Bivariate Analysis

Feature Space and Data Scatter

Pre-Processing and Standardizing Mixed Data

Similarity Data


K-Means Clustering and Related Approaches

Key Concepts

Conventional K-Means

Choice of K and Initialization of K-Means

Intelligent K-Means: Iterated Anomalous Pattern

Minkowski Metric K-Means and Feature Weighting

Extensions of K-Means Clustering

Overall Assessment


Least-Squares Hierarchical Clustering

Key Concepts

Hierarchical Cluster Structures

Agglomeration: Ward Algorithm

Least-Squares Divisive Clustering

Conceptual Clustering

Extensions of Ward Clustering

Overall Assessment


Similarity Clustering: Uniform, Modularity, Additive, Spectral, Consensus and Single Linkage

Key Concepts

Summary Similarity Clustering

Normalized Cut and Spectral Clustering

Additive Clustering

Consensus Clustering

Single Linkage, Minimum Spanning Tree and Connected Components

Overall Assessment


Validation and Interpretation

Key Concepts

General: Internal and External Validity

Testing Internal Validity

Interpretation Aids in the Data Recovery Perspective

Conceptual Description of Clusters

Mapping Clusters to Knowledge

Overall Assessment


Least-Squares Data Recovery Clustering Models

Key Concepts

Statistics Modelling as Data Recovery

K-Means as a Data Recovery Method

Data Recovery Models for Hierarchical Clustering

Data Recovery Models for Similarity Clustering

Consensus and Ensemble Clustering

Overall Assessment


References

Index