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More About This Textbook
Overview
A culmination of the authors’ years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems.
After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering:
This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Product Details
Meet the Author
Bo Long is a scientist at Yahoo! Labs in Sunnyvale, California.
Zhongfei Zhang is an associate professor in the computer science department at the State University of New York in Binghamton.
Philip S. Yu is a professor in the computer science department and the Wexler Chair in Information Technology at the University of Illinois in Chicago.
Table of Contents
List of Tables
List of Figures
Preface
1 Introduction 1
1.1 Defining the Area 1
1.2 The Content and the Organization of This Book 4
1.3 The Audience of This Book 6
1.4 Further Readings 6
I Models 9
2 Co-Clustering 11
2.1 Introduction 11
2.2 Related Work 12
2.3 Model Formulation and Analysis 13
2.3.1 Block Value Decomposition 13
2.3.2 NBVD Method 17
3 Heterogeneous Relational Data Clustering 21
3.1 Introduction 21
3.2 Related Work 23
3.3 Relation Summary Network Model 24
4 Homogeneous Relational Data Clustering 29
4.1 Introduction 29
4.2 Related Work 32
4.3 Community Learning by Graph Approximation 33
5 General Relational Data Clustering 39
5.1 Introduction 39
5.2 Related Work 40
5.3 Mixed Membership Relational Clustering 42
5.4 Spectral Relational Clustering 45
6 Multiple-View Relational Data Clustering 47
6.1 Introduction 47
6.2 Related Work 49
6.3 Background and Model Formulation 50
6.3.1 A General Model for Multiple-View Unsupervised Learning 51
6.3.2 Two Specific Models: Multiple-View Clustering and Multiple-View Spectral Embedding 53
7 Evolutionary Data Clustering 57
7.1 Introduction 57
7.2 Related Work 59
7.3 Dirichlet Process Mixture Chain (DPChain) 60
7.3.1 DPChain Representation 61
7.4 HDP Evolutionary Clustering Model (HDP-EVO) 63
7.4.1 HDP-EVO Representation 63
7.4.2 Two-Level CRP for HDP-EVO 65
7.5 Infinite Hierarchical Hidden Markov State Model 66
7.5.1 iH2 MS Representation 67
7.5.2 Extention of iH2MS 68
7.5.3 Maximum Likelihood Estimation of HTM 69
7.6 HDP Incorporated with HTM (HDP-HTM) 70
7.6.1 Model Representation 70
II Algorithms 73
8 Co-Clustering 75
8.1 Nonnegative Block Value Decomposition Algorithm 75
8.2 Proof of the Correctness of the NBVD Algorithm 78
9 Heterogeneous Relational Data Clustering 83
9.1 Relation Summary Network Algorithm 83
9.2 A Unified View to Clustering Approaches 90
9.2.1 Bipartite Spectral Graph Partitioning 90
9.2.2 Binary Data Clustering with Feature Reduction 90
9.2.3 Information-Theoretic Co-Clustering 91
9.2.4 K-Means Clustering 92
10 Homogeneous Relational Data Clustering 95
10.1 Hard CLGA Algorithm 95
10.2 Soft CLGA Algorithm 97
10.3 Balanced CLGA Algorithm 101
11 General Relational Data Clustering 105
11.1 Mixed Membership Relational Clustering Algorithm 105
11.1.1 MMRC with Exponential Families 105
11.1.2 Monte Carlo E-Step 108
11.1.3 M-Step 109
11.1.4 Hard MMRC Algorithm 112
11.2 Spectral Relational Clustering Algorithm 114
11.3 A Unified View to Clustering 118
11.3.1 Semi-Supervised Clustering 118
11.3.2 Co-Clustering 119
11.3.3 Graph Clustering 120
12 Multiple-View Relational Data Clustering 123
12.1 Algorithm Derivation 123
12.1.1 Multiple-View Clustering Algorithm 124
12.1.2 Multiple-View Spectral Embedding Algorithm 127
12.2 Extensions and Discussions 129
12.2.1 Evolutionary Clustering 129
12.2.2 Unsupervised Learning with Side Information 130
13 Evolutionary Data Clustering 133
13.1 DPChain Inference 133
13.2 HDP-EVO Inference 134
13.3 HDP-HTM Inference 136
III Applications 139
14 Co-Clustering 141
14.1 Data Sets and Implementation Details 141
14.2 Evaluation Metricees 142
14.3 Results and Discussion 143
15 Heterogeneous Relational Data Clustering 147
15.1 Data Sets and Parameter Setting 147
15.2 Results and Discussion 150
16 Homogeneous Relational Data Clustering 153
16.1 Data Sets and Parameter Setting 153
16.2 Results and Discussion 155
17 General Relational Data Clustering 159
17.1 Graph Clustering 159
17.2 Bi-clustering and Tri-Clustering 161
17.3 A Case Study on Actor-Movie Data 163
17.4 Spectral Relational Clustering Applications 164
17.4.1 Clustering on Bi-Type Relational Data 164
17.4.2 Clustering on Tri-Type Relational Data 166
18 Multiple-View and Evolutionary Data Clustering 169
18.1 Multiple-View Clustering 169
18.1.1 Synthetic Data 169
18.1.2 Real Data 172
18.2 Multiple-View Spectral Embedding 173
18.3 Semi-Supervised Clustering 174
18.4 Evolutionary Clustering 175
IV Summary 179
References 185
Index 195