A Sampling-Based Framework for Parallel Mining Frequent Patterns
Cong, Shengnan
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Permalink
https://hdl.handle.net/2142/81710
Description
Title
A Sampling-Based Framework for Parallel Mining Frequent Patterns
Author(s)
Cong, Shengnan
Issue Date
2006
Doctoral Committee Chair(s)
David Padua
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
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.
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