关键词: K-Modes聚类, 属性选择, 自动属性赋权

Abstract: One major problem of the traditional K-Modes algorithm is the selection of features. The K-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 pro¬cess and implement the selection of key features. The experimental results on several UCI machine learning data sets validate the effectiveness of the proposed algorithm.

Key words: K-Modes clustering, feature selection, automated feature weighting