Incremental Mining with Prelarge Trees

Authors:
Chun-Wei Lin;Tzung-Pei Hong;Wen-Hsiang Lu;Been-Chian Chien
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. 701;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 Information Engineering, National University of Tainan, Tainan, Taiwan, R.O.C. 700
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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Abstract

In the past, we proposed a Fast Updated FP-tree (FUFP-tree) structure to efficiently handle new transactions and to make the tree-update process become easy. In this paper, we propose the structure of prelarge trees to incrementally mine association rules based on the concept of pre-large itemsets. Due to the properties of pre-large concepts, the proposed approach does not need to rescan the original database until a number of new transactions have been inserted. Experimental results also show that the proposed approach has a good performance for incrementally handling new transactions.