Incrementally mining high utility patterns based on pre-large concept

Authors:
Chun-Wei Lin;Tzung-Pei Hong;Guo-Cheng Lan;Jia-Wei Wong;Wen-Yang Lin
Affiliations:
Innovative Information Industry Research Center (IIIRC), Harbin Institute of Technology Shenzhen Graduate School, Xili, P.R. China 518055 and Shenzhen Key Laboratory of Internet Information Collab ...;Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C. 811 and Department of Computer Science and Engineering, National Sun Yat-sen ...;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. 701;Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C. 804;Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C. 811
Venue:
Applied Intelligence
Year:
2014

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Abstract

In traditional association rule mining, most algorithms are designed to discover frequent itemsets from a binary database. Utility mining was thus proposed to measure the utility values of purchased items for revealing high utility itemsets from a quantitative database. In the past, a two-phase high utility mining algorithm was thus proposed for efficiently discovering high utility itemsets from a quantitative database. In dynamic data mining, transactions may be inserted, deleted, or modified from a database. In this case, a batch mining procedure must rescan the whole updated database to maintain the up-to-date information. Designing an efficient approach for handling dynamic databases is thus a critical research issue in utility mining. In this paper, an incremental mining algorithm is proposed for efficiently maintaining discovered high utility itemsets based on pre-large concepts. Itemsets are first partitioned into three parts according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in inserted transactions. Individual procedures are then executed for each part. Experimental results show that the proposed incremental high utility mining algorithm outperforms existing algorithms.