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Relational Data Clustering

- Models, Algorithms, and Applications

By Bo Long
About Relational Data Clustering

A culmination of the authorsΓÇÖ 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.

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  • Language:
  • English
  • ISBN:
  • 9780367384050
  • Binding:
  • Paperback
  • Pages:
  • 216
  • Published:
  • September 19, 2019
  • Dimensions:
  • 156x234x0 mm.
  • Weight:
  • 453 g.
Delivery: 1-2 weeks
Expected delivery: October 19, 2024

Description of Relational Data Clustering

A culmination of the authorsΓÇÖ 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.

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