Maintenance of fast updated frequent trees for record deletion based on prelarge concepts

Authors:
Chun-Wei Lin;Tzung-Pei Hong;Wen-Hsiang Lu;Chih-Hung Wu
Affiliations:
Department of Computer Science and Information Engineering National Cheng Kung University Tainan, Taiwan, R.O.C.;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C.;Department of Computer Science and Information Engineering National Cheng Kung University Tainan, Taiwan, R.O.C.;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C.
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

The frequent pattern tree (FP-tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It, however, needed to process all transactions in a batch way. In the past, we proposed the Fast Updated FP-tree (FUFP-tree) structure to efficiently handle the newly inserted transactions in incremental mining. In this paper, we attempt to modify the FUFP-tree maintenance based on the concept of pre-large itemsets for efficiently handling deletion of records. Pre-large itemsets are defined by a lower support threshold and an upper support threshold. The proposed approach can thus achieve a good execution time for tree maintenance especially when each time a small number of records are deleted. Experimental results also show that the proposed Pre-FUFP deletion algorithm has a good performance for incrementally handling deleted records.