A new incremental data mining algorithm using pre-large itemsets

  • Authors:

  • Tzung-Pei Hong;Ching-Yao Wang;Yu-Hui Tao

  • Affiliations:

  • Department of Information Management, I-Shou University, Kaohsiung, 84008, Taiwan, ROC. E-mail: tphong@isu.edu.tw/ URL: http://www.nuk.edu.tw/tphong;Institute of Computer and Information Science, National Chiao-Tung University, Hsinchu, 300, Taiwan, ROC. E-mail: cywang@cis.nctu.edu.tw;Department of Information Management, I-Shou University, Kaohsiung, 84008, Taiwan, ROC. E-mail: ytao@isu.edu.tw

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2001

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Abstract

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. In the past, researchers usually assumed databases were static to simplify data mining problems. Thus, most of the classic algorithms proposed focused on batch mining, and did not utilize previously mined information in incrementally growing databases. In real-world applications, however, developing a mining algorithm that can incrementally maintain discovered information as a database grows is quite important. In this paper, we propose the concept of pre-large itemsets and design a novel, efficient, incremental mining algorithm based on it. Pre-large itemsets are defined by a lower support threshold and an upper support threshold. They act as gaps to avoid the movements of itemsets directly from large to small and vice-versa. The proposed algorithm doesn't need to rescan the original database until a number of transactions have been newly inserted. If the database has grown larger, then the number of new transactions allowed will be larger too.