New algorithms for efficient mining of association rules

Li Shen, Hong Shen, Ling Cheng

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Discovery of association rules is an important data mining task. Several algorithms have been proposed to solve this problem. Most of them require repeated passes over the database, which incurs huge I/O overhead and high synchronization expense in parallel cases. There are a few algorithms trying to reduce these costs. But they contains weaknesses such as often requiring high pre-processing cost to get a vertical database layout, containing much redundant computation in parallel cases, and so on. We propose new association mining algorithms to overcome the above drawbacks: through minimizing the I/O cost and effectively controlling the computation cost. Experiments on well-known synthetic data show that our algorithms consistently outperform a priori, one of the best algorithms for association mining, by factors ranging from 2 to 4 in most cases. Also, our algorithms are very easy to be parallelized, and we present a parallelization for them based on a shared-nothing architecture. We observe that the parallelism in our parallel approach is developed more sufficiently than in two of the best existing parallel algorithms.

原文English
主出版物標題Proceedings - Frontiers 1999, 7th Symposium on the Frontiers of Massively Parallel Computation
發行者Institute of Electrical and Electronics Engineers Inc.
頁面234-241
頁數8
ISBN(電子)0769500870, 9780769500874
DOIs
出版狀態Published - 1999
對外發佈
事件7th Symposium on the Frontiers of Massively Parallel Computation, Frontiers 1999 - Annapolis, United States
持續時間: 21 2月 199925 2月 1999

出版系列

名字Proceedings - Frontiers 1999, 7th Symposium on the Frontiers of Massively Parallel Computation

Conference

Conference7th Symposium on the Frontiers of Massively Parallel Computation, Frontiers 1999
國家/地區United States
城市Annapolis
期間21/02/9925/02/99

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