Mining High Utility Itemsets Based on Transaction Deletion
In the past, an incremental algorithm for mining high utility itemsets was proposed to derive high utility itemsets in an incrementally inserted way. In real-world applications, transactions are not only inserted into but also deleted from a database. In
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Abstract In the past, an incremental algorithm for mining high utility itemsets was proposed to derive high utility itemsets in an incrementally inserted way. In real-world applications, transactions are not only inserted into but also deleted from a database. In this paper, a maintenance algorithm is thus proposed for reducing the execution time of maintaining high utility itemsets due to transaction deletion. Experimental results also show that the proposed maintenance algorithm runs much faster than the batch approach. Keywords Utility mining approach FUP concept
Maintenance
Transaction deletion
Two-phase
C.-W. Lin (&) L. Kong IIIRC, School of Computer Science and Technology, Institute of Technology Shenzhen Graduate School, Xili, Shenzhen, People’s Republic of China e-mail: [email protected] L. Kong e-mail: [email protected] C.-W. Lin Shenzhen Key Laboratory of Internet Information Collaboration Harbin, Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, People’s Republic of China G.-C. Lan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, Republic of China e-mail: [email protected] T.-P. Hong Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, Republic of China e-mail: [email protected] T.-P. Hong Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, Republic of China
Y.-M. Huang et al. (eds.), Advanced Technologies, Embedded and Multimedia for Human-centric Computing, Lecture Notes in Electrical Engineering 260, DOI: 10.1007/978-94-007-7262-5_112, Ó Springer Science+Business Media Dordrecht 2014
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Introduction Association rules mining from binary database is the fundamental approach for knowledge discovery [1–4]. In some applications, frequent itemsets may only contribute a small portion to the overall profit, while non-frequent ones may contribute a large portion to the profit [5]. In the past, utility mining [6] was thus proposed to partially solve the above limitations. It may be thought of as an extension of frequent itemset mining with the sold quantities as a quantitative database with the item profits considered [5, 7–10]. In real-world applications, transactions in the database do not usually remain in a stable condition. Some transactions may be inserted or deleted from an original database. The discovered frequent itemsets may become invalid, or some new implicit information may emerge in the whole updated database. Lin et al. thus proposed an incremental algorithm for transaction insertion in high utility mining [11] based on the Fast UPdated (FUP) concepts [12]. In addition to transaction insertion, transaction deletion is also commonly seen in real-world applications. In this paper, a maintenance algorithm is thus proposed to update the mined utility itemsets for deleted transactions. The FUP2 (Fast UPdated) algorithm [13] is the
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