文章基本信息

标题：IWFPM: Interested Weighted Frequent Pattern Mining with Multiple Supports
其他标题：IWFPM: Interested Weighted Frequent Pattern Mining with Multiple Supports
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作者：Xuyang Wei ; Zhongliang Li ; Tengfei Zhou 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2015
卷号：10
期号：1
页码：9-19
DOI：10.17706/jsw.10.1.9-19
出版社：Academy Publisher
摘要：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.
其他关键词：Data mining, frequent pattern mining, association rules technology, interest.