دانلود کتاب Theory of Agglomerative Hierarchical Clustering (Behaviormetrics: Quantitative Approaches to Human Behavior, 15)

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Preface
Contents
1 Introduction
1.1 Notations
1.2 Informal Procedure
1.3 K-means as a Method of Non Hierarchical Clustering
1.4 Similarity or Dissimilarity Measures
1.5 Simple Examples
1.6 Hierarchical Partitions, Relations, and Dissimilarity Measures
References
2 Linkage Methods and Algorithms
2.1 Formal and Abstract Procedure
2.2 Linkage Methods
2.2.1 Similarity or Dissimilarity Measures Between Clusters
2.2.2 Proof of Updating Formulas
2.3 Examples
2.4 Output of Dendrogram
2.4.1 Tree as Data Structure for Dendrogram
2.4.2 Drawing Dendrograms
2.5 Problems and Theoretical Issues
2.5.1 Reversals: Monotone Property of Merging Levels
2.5.2 Similar Dendrograms
References
3 Theory of the Single Linkage Method
3.1 Network Algorithm and the Single Linkage
3.2 Max–Min Composition and Transitive Closure
3.3 Transitive Closure and Fuzzy Graph
3.4 An Illustrative Example
3.5 A Refinement Theory
3.6 A Variation of the Single Linkage Method
References
4 Positive-Definite Kernels in Agglomerative Hierarchical Clustering
4.1 Positive-Definite Kernels
4.2 Linkage Methods Using Kernels
4.3 Indefinite Similarity Matrix for Ward Method
References
5 Some Other Topics in Agglomerative Hierarchical Clustering
5.1 Single Linkage, DBSCAN, and Mode Analysis
5.1.1 DBSCAN and the Single Linkage
5.1.2 Mode Analysis and the Single Linkage
5.2 Clustering and Classification
5.2.1 Two-Stage Clustering
5.3 Constrained Agglomerative Hierarchical Clustering
5.3.1 An Illustrative Example
5.4 Model-Based Clustering and Agglomerative Hierarchical Algorithms
5.5 Agglomerative Clustering Using Asymmetric Measures
References
6 Miscellanea
6.1 Two Other Linkage Methods
6.1.1 Lance–Williams Formula
6.2 More on Ward Method
6.2.1 Merging Levels of Ward Method
6.2.2 Divisive Clustering and X-means
6.2.3 Some Similarity Measures and Ward Method
6.3 Handling Weighted Objects
6.4 Discussion and Future Works
References
Index