基于模式增长方式的高效用模式挖掘算法

王乐; 熊松泉; 常艳芬; 王水

doi:10.16383/j.aas.2015.c150056

基于模式增长方式的高效用模式挖掘算法

doi: 10.16383/j.aas.2015.c150056

1.
宁波大红鹰学院信息工程学院宁波 315175

基金项目:

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

详细信息

作者简介:
王乐宁波大红鹰学院信息工程学院讲师.2013年获得大连理工大学博士学位.主要研究方向为数据挖掘.E-mail:wangleboro@163.com

熊松泉宁波大红鹰学院信息工程学院副教授.2012年获得同济大学硕士学位.主要研究方向为数据挖掘和嵌入式系统.E-mail:yisan-sky@qq.com

常艳芬宁波大红鹰学院信息工程学院讲师.2009年获得同济大学硕士学位.主要研究方向为数据挖掘和软件工程.E-mail:cyf511@163.com

通讯作者:
王水宁波大红鹰学院信息工程学院教授.1989年获得兰州大学学士学位.主要研究方向为数据挖掘和软件工程.本文通信作者.E-mail:seawan@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2015-01-30
- 修回日期: 2015-06-07
- 刊出日期: 2015-09-20

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

1.
School of Information Engineering, Ningbo Dahongying University, Ningbo 315175

Funds:

Supported by Ningbo Natural Science Foundation & Research Program (2013A610115, 2014A610073, 2013C10010), General Scientific Research Fund of Zhejiang Provincial Education Department (Y201432717),and Bulk Commodity Special Fund of Ningbo Dahongying University (1320133004)

摘要

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.
- 高效用模式 /
- 频繁模式 /
- 频繁项集 /
- 数据挖掘
Abstract: 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.
- High utility pattern /
- frequent pattern /
- frequent itemset /
- data mining

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