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Books > Computing & IT > Applications of computing > Databases > Data mining

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Relational Data Clustering - Models, Algorithms, and Applications (Paperback) Loot Price: R1,886
Discovery Miles 18 860
Relational Data Clustering - Models, Algorithms, and Applications (Paperback): Bo Long, Zhongfei Zhang, Philip S. Yu

Relational Data Clustering - Models, Algorithms, and Applications (Paperback)

Bo Long, Zhongfei Zhang, Philip S. Yu

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Loot Price R1,886 Discovery Miles 18 860 | Repayment Terms: R177 pm x 12*

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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.

General

Imprint: Crc Press
Country of origin: United Kingdom
Release date: September 2019
First published: 2010
Authors: Bo Long • Zhongfei Zhang • Philip S. Yu
Dimensions: 234 x 156mm (L x W)
Format: Paperback
Pages: 216
ISBN-13: 978-0-367-38405-0
Categories: Books > Computing & IT > Applications of computing > Databases > Data mining
LSN: 0-367-38405-1
Barcode: 9780367384050

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