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University of Illinois at Urbana-Champaign
A Sampling-Based Framework for Parallel Mining Frequent Patterns
Abstract
dc:descriptionWe 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.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Cong, Shengnan
- Contributors dc:contributor
-
- David Padua
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3223570
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81710