计算机科学与探索2012，Vol.6Issue(1)：90-96,7.DOI:10.3778/j.issn.1673-9418.2012.01.007
属性赋权的K-Modes算法优化
Optimization of K-Modes Algorithm with Feature Weights
摘要
Abstract
One major problem of the traditional K-Modes algorithm is the selection of features. The ￡-Modes clustering algorithm treats all features equally in the clustering process. But in practice, there are only a few important features in many data sets. To consider the particular contribution of different attributes, this paper proposes an improved algorithm called FW-K-Modes algorithm, which incorporates the K-Modes clustering algorithm with feature weight optimization. The proposed algorithm can not only improve the clustering precision in comparison with the traditional K-Modes clustering algorithm, but also analyze the important level of each feature in the clustering process and implement the selection of key features. The experimental results on several UCI machine learning data sets validate the effectiveness of the proposed algorithm.关键词
K-Modes聚类/属性选择/自动属性赋权Key words
K-Modes clustering/ feature selection/ automated feature weighting分类
信息技术与安全科学引用本文复制引用
李仁侃,叶东毅..属性赋权的K-Modes算法优化[J].计算机科学与探索,2012,6(1):90-96,7.基金项目
The Natural Science Foundation of Fujian Province of China under Grant No.2010J01329(福建省自然科学基金) （福建省自然科学基金）
the Key Science and Technology Project of Fujian Province of China under Grant Nos.2010H6012,2009J1007(福建省科技重点项目). （福建省科技重点项目）
