Long / Zhang / Yu Relational Data Clustering

Models, Algorithms, and Applications
Erscheinungsjahr 2010
ISBN: 978-1-4200-7262-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 216 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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:

- Clustering on bi-type heterogeneous relational data

- Multi-type heterogeneous relational data

- Homogeneous relational data clustering

- Clustering on the most general case of relational data

- Individual relational clustering framework

- 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.

Zielgruppe

Computer scientists, engineers, researchers, and graduate students in data mining, machine learning, computer vision, pattern recognition, and statistics.

Autoren/Hrsg.

Long, Bo

Zhang, Zhongfei

Yu, Philip S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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

Über Autor(innen)

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.