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Partitional Clustering Algorithms

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Cover of 'Partitional Clustering Algorithms'

Table of Contents

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    Book Overview
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    Chapter 1 Recent Developments in Model-Based Clustering with Applications
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    Chapter 2 Accelerating Lloyd’s Algorithm for k -Means Clustering
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    Chapter 3 Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
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    Chapter 4 Nonsmooth Optimization Based Algorithms in Cluster Analysis
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    Chapter 5 Fuzzy Clustering Algorithms and Validity Indices for Distributed Data
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    Chapter 6 Density Based Clustering: Alternatives to DBSCAN
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    Chapter 7 Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering
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    Chapter 8 Overview of Overlapping Partitional Clustering Methods
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    Chapter 9 On Semi-Supervised Clustering
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    Chapter 10 Consensus of Clusterings Based on High-Order Dissimilarities
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    Chapter 11 Hubness-Based Clustering of High-Dimensional Data
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    Chapter 12 Clustering for Monitoring Distributed Data Streams
Attention for Chapter 3: Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
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