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Title:Scalable Mining and Link Analysis Across Multiple Database Relations
Author(s):Yin, Xiaoxin
Doctoral Committee Chair(s):Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
Abstract:Because of the complexity of multi-relational data, efficiency and scalability are two major concerns in multi-relational data mining. In this thesis we propose scalable and accurate approaches for each data mining task studied. In order to achieve high efficiency and scalability, the approaches utilize novel techniques for virtually joining different relations, single-scan algorithms, and multi-resolutional data structures to dramatically reduce computational costs. Our experiments show that our approaches are highly efficient and scalable, and also achieve high accuracies in multi-relational data mining.
Issue Date:2007
Type:Text
Language:English
Description:154 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
URI:http://hdl.handle.net/2142/81770
Other Identifier(s):(MiAaPQ)AAI3270062
Date Available in IDEALS:2015-09-25
Date Deposited:2007


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