A CLUSTERING ALGORITHM FOR MIXED NUMERIC AND CATEGORICAL DATA

作     者：Ohn Mar San Van-Nam Huynh Yoshiteru Nakamori 

作者机构：School of Knowledge Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Tatsunokuchi Ishikawa 923-1292 JapanSchool of Knowledge Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Tatsunokuchi Ishikawa 923-1292 JapanSchool of Knowledge Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Tatsunokuchi Ishikawa 923-1292 Japan 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2003年第16卷第4期

页      面：562-571页

核心收录：

学科分类：07[理学] 070102[理学-计算数学] 0701[理学-数学] 

主　　题：数据挖掘 数字数据 分类数据 聚类算法 数据库 数据集 

摘      要：Most of the earlier work on clustering mainly focused on numeric data whose inherent geometric properties can be exploited to naturally define distance functions between data points. However, data mining applications frequently involve many datasets that also consists of mixed numeric and categorical attributes. In this paper we present a clustering algorithm which is based on the k-means algorithm. The algorithm clusters objects with numeric and categorical attributes in a way similar to k-means. The object similarity measure is derived from both numeric and categorical attributes. When applied to numeric data, the algorithm is identical to the k-means. The main result of this paper is to provide a method to update the cluster centers of clustering objects described by mixed numeric and categorical attributes in the clustering process to minimise the clustering cost function. The clustering performance of the algorithm is demonstrated with the two well known data sets, namely credit approval and abalone databases.