Handling Dynamic Weights in Weighted Frequent Pattern Mining

Chowdhury Farhan AHMED  Syed Khairuzzaman TANBEER  Byeong-Soo JEONG  Young-Koo LEE  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.11   pp.2578-2588
Publication Date: 2008/11/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.11.2578
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)
Category: Knowledge Discovery and Data Mining
Keyword: 
data mining,  knowledge discovery,  weighted frequent pattern mining,  dynamic weight,  

Full Text: PDF(747.7KB)>>
Buy this Article




Summary: 
Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.