A k-mean clustering algorithm for mixed numeric and categorical data

作者：Ahmad Amir^*; Dey Lipika

来源：Data & Knowledge Engineering, 2007, 63(2): 503-527.

DOI：10.1016/j.datak.2007.03.016

• clustering
• k-mean clustering
• cost function
• distance measure
• significance of attributes
• co-occurrences

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach.

• 出版日期2007-11