Efficient Dynamic Weighted Frequent Pattern Mining by using a Prefix-Tree


The KIPS Transactions:PartD, Vol. 17, No. 4, pp. 253-258, Aug. 2010
10.3745/KIPSTD.2010.17.4.253,   PDF Download:

Abstract

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.


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Cite this article
[IEEE Style]
B. S. Jeong, "Efficient Dynamic Weighted Frequent Pattern Mining by using a Prefix-Tree," The KIPS Transactions:PartD, vol. 17, no. 4, pp. 253-258, 2010. DOI: 10.3745/KIPSTD.2010.17.4.253.

[ACM Style]
Byeong Soo Jeong. 2010. Efficient Dynamic Weighted Frequent Pattern Mining by using a Prefix-Tree. The KIPS Transactions:PartD, 17, 4, (2010), 253-258. DOI: 10.3745/KIPSTD.2010.17.4.253.