Scalable Mining and Link Analysis Across Multiple Database Relations
Yin, Xiaoxin
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https://hdl.handle.net/2142/81770
Description
Title
Scalable Mining and Link Analysis Across Multiple Database Relations
Author(s)
Yin, Xiaoxin
Issue Date
2007
Doctoral Committee Chair(s)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
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.
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