A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments

Kawuu W. Lin, Der Jiunn Deng

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

Parallel and distributed computing techniques have attracted extensive attentions on the ability to manage and compute the significant amount of data in the past decades. The difficulty of mining large database launched the research of designing parallel and distributed algorithms to solve the problem. In this paper, we propose a novel data mining algorithm, named Cloud-based Association Rule Mining (CARM), abbreviated as CARM, which is able to efficiently utilise the nodes to discover frequent patterns in cloud computing environments with data privacy preserved. Through empirical evaluations on various simulation conditions, the proposed CARM delivers excellent performance in terms of scalability and execution time.

Original languageEnglish
Pages (from-to)205-215
Number of pages11
JournalInternational Journal of Ad Hoc and Ubiquitous Computing
Volume6
Issue number4
DOIs
Publication statusPublished - 2010 Jan 1

Fingerprint

Association rules
Cloud computing
Parallel algorithms
Data privacy
Distributed computer systems
Parallel processing systems
Data mining
Scalability

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

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