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Relational data clustering.

State University of New York at Binghamton., Computer Science.

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Relational data clustering.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Relational data clustering./
作者:	Long, Bo.
面頁冊數:	162 p.
附註:	Adviser: Zhongfei M. Zhang.
Contained By:	Dissertation Abstracts International69-12B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3338795
ISBN:	9780549931461

Relational data clustering.
Long, Bo.

Relational data clustering. - 162 p.

Adviser: Zhongfei M. Zhang.

Thesis (Ph.D.)--State University of New York at Binghamton, 2008.

In many important applications, it is typical that data objects do not exist in isolation; instead, it is ubiquitous that they exist through relations. More importantly, it is the relations among objects that are of crucial importance to pattern discovery. On the other hand, there is often no such luxury to have any training data in a learning task. Consequently, learning cluster structures from those interrelated objects (multi-type or single type), relational data clustering, has become one of the most important data mining and machine learning topics in both industry and academia, though it is still a fairly new topic. In general, relational data contain three types of information, heterogeneous relations between objects of different types, homogeneous relations between objects of the same type, and attributes for individual objects. Our work focuses on developing a unified theoretical framework for relational data clustering and effective algorithms for different cases of relational data, including bi-type heterogeneous relational data, heterogeneous relational data, homogeneous relational data, and general relational data.

ISBN: 9780549931461Subjects--Topical Terms:

626642
Computer Science.

Relational data clustering.
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520 $a In many important applications, it is typical that data objects do not exist in isolation; instead, it is ubiquitous that they exist through relations. More importantly, it is the relations among objects that are of crucial importance to pattern discovery. On the other hand, there is often no such luxury to have any training data in a learning task. Consequently, learning cluster structures from those interrelated objects (multi-type or single type), relational data clustering, has become one of the most important data mining and machine learning topics in both industry and academia, though it is still a fairly new topic. In general, relational data contain three types of information, heterogeneous relations between objects of different types, homogeneous relations between objects of the same type, and attributes for individual objects. Our work focuses on developing a unified theoretical framework for relational data clustering and effective algorithms for different cases of relational data, including bi-type heterogeneous relational data, heterogeneous relational data, homogeneous relational data, and general relational data.
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