基于模式增长方式的高效用模式挖掘算法  

An Algorithm for Mining High Utility Patterns Based on Pattern-growth

作　　者：王乐[1] 熊松泉[1] 常艳芬[1] 王水[1] 

机构地区：[1]宁波大红鹰学院信息工程学院,宁波315175

出　　处：《自动化学报》2015年第9期1616-1626,共11页Acta Automatica Sinica

基　　金：宁波市自然科学基金和攻关项目(2013A610115;2014A610073;2013C10010);浙江省教育厅一般科研项目(Y201432717);宁波大红鹰学院大宗商品专项课题(1320133004)资助~~

摘　　要：高效用模式挖掘是数据挖掘领域的一个重要研究内容;由于其计算过程包含对模式的内、外效用值的处理,计算复杂度较大,因此挖掘算法的主要研究热点问题就是提高算法的时间效率.针对此问题,本文给出一个基于模式增长方式的高效用模式挖掘算法HUPM-FP,该算法可以从全局树上挖掘高效用模式,避免产生候选项集.实验中,采用6个典型数据集进行实验,并和目前效率较好的算法FHM(Faster high-utility itemset mining)做了对比,实验结果表明本文给出的算法时空效率都有较大的提高,特别是时间效率提高较大,可以达到1个数量级以上.High utility pattern mining is an important research topic in data mining. Because of the additional inner/outer utility processing workload, its computational complexity increases, and the improvement of its temporal efficiency is vital. To address this issue, a new pattern-growth mining algorithm is proposed for high utility pattern mining named HUPM-FP. This algorithm can mine high utility patterns from a global tree without generating candidate itemsets.Six classical datasets were used in our experiments for comparing with the state-of-art algorithm faster high-utility itemset mining（FHM）. The proposed HUPM-FP out-performed its counterpart significantly, especially for time efficiency, which was up to 1 order of magnitude faster.

关 键 词：高效用模式 频繁模式 频繁项集 数据挖掘 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]