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Clustering: A Data Recovery Approach

Edition: 
2
Publisher: 
Chapman & Hall/CRC
Number of Pages: 
350
Price: 
99.95
ISBN: 
9781439838419
Date Received: 
Wednesday, November 28, 2012
Reviewable: 
Include In BLL Rating: 
Boris Mirkin
Series: 
Computer Science and Data Analysis Series
Publication Date: 
2013
Format: 
Hardcover
Category: 
Monograph

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

Publish Book: 
Modify Date: 
Wednesday, November 28, 2012

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