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Title:A Sampling-Based Framework for Parallel Mining Frequent Patterns
Author(s):Cong, Shengnan
Doctoral Committee Chair(s):David Padua
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:We implemented parallel algorithms for mining frequent itemsets, sequential patterns and closed-sequential patterns following our framework. A comprehensive performance study has been conducted in our experiments on both synthetic and real-world datasets. The experimental results have shown that our parallel algorithms have achieved good speedups on various datasets and the speedups are scalable up to 64 processors on our 64-processor system.
Issue Date:2006
Type:Text
Language:English
Description:96 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
URI:http://hdl.handle.net/2142/81710
Other Identifier(s):(MiAaPQ)AAI3223570
Date Available in IDEALS:2015-09-25
Date Deposited:2006


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