An improved k-prototypes clustering algorithm for mixed numeric and categorical data

• Published in: Neurocomputing (Amsterdam) Vol. 120; pp. 590 - 596
• Main Authors: Ji, Jinchao, Bai, Tian, Zhou, Chunguang, Ma, Chao, Wang, Zhe
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 23.11.2013; Elsevier
• Subjects: Algorithms; Applied sciences; Attribute significance; Clustering; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Dissimilarity measure; Exact sciences and technology; Memory organisation. Data processing; Mixed data; Software; Dissimilarity measure; Clustering; Data mining; Mixed data; Attribute significance; Cluster analysis; Data type; Data analysis; Prototype; Similarity; Cluster; Categorical data; Center of mass; Selection criterion
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.04.011

Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principal algorithms for clustering this type of data objects. In this paper, we propose an improved k-prototypes algorithm to cluster mixed data. In our method, we first introduce the concept of the distribution centroid for representing the prototype of categorical attributes in a cluster. Then we combine both mean with distribution centroid to represent the prototype of the cluster with mixed attributes, and thus propose a new measure to calculate the dissimilarity between data objects and prototypes of clusters. This measure takes into account the significance of different attributes towards the clustering process. Finally, we present our algorithm for clustering mixed data, and the performance of our method is demonstrated by a series of experiments on four real-world datasets in comparison with that of traditional clustering algorithms. •We propose a new representation for the prototype of a cluster with mixed attributes.•We give a new measure to assess the dissimilarity between data objects and prototype.•This measure considers the significance of attribute towards clustering process.•Our algorithm can calculate the significance of attribute towards clustering.•Our algorithm achieves better results according to the clustering accuracy.