Volume 10 Number 1 (Jan. 2015)
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JSW 2015 Vol.10(1): 9-19 ISSN: 1796-217X
doi: 10.17706/jsw.10.1.9-19

IWFPM: Interested Weighted Frequent Pattern Mining with Multiple Supports

Xuyang Wei, Zhongliang Li, Tengfei Zhou, Haoran Zhang, Guocai Yang
School of Computer and Information Science, Southwest University, Chongqing, China.
Abstract—Association rules mining has been under great attention and considered as one of momentous area in data mining. Classical association rules mining approaches make implicit assumption that items’ importance is the same and set a single support for all items. This paper presents an efficient approach for mining users’ interest weighted frequent patterns from a transactional database. Our paradigm is to assign appropriate minimum support (minsup) and weight for each item, which reduces the number of unnecessary patterns. Furthermore, we also extend the support-confidence framework and define an interest measure to the mining algorithm for excavating users’ interested patterns effectively. In the end, experiments on both synthetic and real world datasets show that the proposed algorithm can generate more interested patterns.

Index Terms—Data mining, frequent pattern mining, association rules technology, interest.


Cite: Xuyang Wei, Zhongliang Li, Tengfei Zhou, Haoran Zhang, Guocai Yang, "IWFPM: Interested Weighted Frequent Pattern Mining with Multiple Supports," Journal of Software vol. 10, no. 1, pp. 9-19, 2015.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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