Relational Data Clustering: Models, Algorithms, and Applications, 1st Edition (Hardback) book cover

Relational Data Clustering

Models, Algorithms, and Applications, 1st Edition

By Bo Long, Zhongfei Zhang, Philip S. Yu

Chapman and Hall/CRC

216 pages | 30 B/W Illus.

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Hardback: 9781420072617
pub: 2010-05-19
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Description

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:

  1. Clustering on bi-type heterogeneous relational data
  2. Multi-type heterogeneous relational data
  3. Homogeneous relational data clustering
  4. Clustering on the most general case of relational data
  5. Individual relational clustering framework
  6. Recent research on evolutionary 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.

Table of Contents

Introduction

MODELS

Co-Clustering

Introduction

Related Work

Model Formulation and Analysis

Heterogeneous Relational Data Clustering

Introduction

Related Work

Relation Summary Network Model

Homogeneous Relational Data Clustering

Introduction

Related Work

Community Learning by Graph Approximation

General Relational Data Clustering

Introduction

Related Work

Mixed Membership Relational Clustering

Spectral Relational Clustering

Multiple-View Relational Data Clustering

Introduction

Related Work

Background and Model Formulation

Evolutionary Data Clustering

Introduction

Related Work

Dirichlet Process Mixture Chain (DPChain)

HDP Evolutionary Clustering Model (HDP-EVO)

HDP Incorporated with HTM (HDP-HTM)

ALGORITHMS

Co-Clustering

Nonnegative Block Value Decomposition (NBVD) Algorithm

Proof of the Correctness of the NBVD Algorithm

Heterogeneous Relational Data Clustering

Relation Summary Network Algorithm

A Unified View to Clustering Approaches

Homogeneous Relational Data Clustering

Hard CLGA Algorithm

Soft CLGA Algorithm

Balanced CLGA Algorithm

General Relational Data Clustering

Mixed Membership Relational Clustering Algorithm

Spectral Relational Clustering Algorithm

A Unified View to Clustering

Multiple-View Relational Data Clustering

Algorithm Derivation

Extensions and Discussions

Evolutionary Data Clustering

DPChain Inference

HDP-EVO Inference

HDP-HTM Inference

APPLICATIONS

Co-Clustering

Data Sets and Implementation Details

Evaluation Metrics

Results and Discussion

Heterogeneous Relational Data Clustering

Data Sets and Parameter Setting

Results and Discussion

Homogeneous Relational Data Clustering

Data Sets and Parameter Setting

Results and Discussion

General Relational Data Clustering

Graph Clustering

Bi-Clustering and Tri-Clustering

A Case Study on Actor-Movie Data

Spectral Relational Clustering Applications

Multiple-View and Evolutionary Data Clustering

Multiple-View Clustering

Multiple-View Spectral Embedding

Semi-Supervised Clustering

Evolutionary Clustering

SUMMARY

References

Index

About the Authors

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.

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM000000
COMPUTERS / General
COM021030
COMPUTERS / Database Management / Data Mining
MAT029000
MATHEMATICS / Probability & Statistics / General