Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40204
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dc.titleSpeed-up iterative frequent itemset mining with constraint changes
dc.contributor.authorCong, G.
dc.contributor.authorLiu, B.
dc.date.accessioned2013-07-04T07:59:00Z
dc.date.available2013-07-04T07:59:00Z
dc.date.issued2002
dc.identifier.citationCong, G.,Liu, B. (2002). Speed-up iterative frequent itemset mining with constraint changes. Proceedings - IEEE International Conference on Data Mining, ICDM : 107-114. ScholarBank@NUS Repository.
dc.identifier.isbn0769517544
dc.identifier.issn15504786
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40204
dc.description.abstractMining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for the purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in enormous waste in time and in compulation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize the useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the contexts of two existing frequent itemset mining algorithms, FP-tree and Tree Projection. Experiment results on both synthetic and real-life datasets show that the proposed approach achieves dramatic saving in computation. © 2002 IEEE.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings - IEEE International Conference on Data Mining, ICDM
dc.description.page107-114
dc.identifier.isiutNOT_IN_WOS
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