Clustering: A Data Recovery Approach
Mirkin Boris · CRC Press eBooks · 2012
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