An incremental mixed data clustering method using a new distance measure

作     者：Noorbehbahani, Fakhroddin Mousavi, Sayyed Rasoul Mirzaei, Abdolreza 

作者机构：Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2015年第19卷第3期

页      面：731-743页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Mixed data clustering Incremental learning Distance measure Clustering evaluation measures SOM 

摘      要：Clustering is one of the most applied unsupervised machine learning tasks. Although there exist several clustering algorithms for numeric data, more sophisticated clustering algorithms to address mixed data (numeric and categorical data) more efficiently are still required. Other important issues to be considered in clustering are incremental learning and generating a sufficient number of clusters without specifying the number of clusters a priori. In this paper, we introduce a mixed data clustering method which is incremental and generates a sufficient number of clusters automatically. The proposed method is based on the Adjusted Self-Organizing Incremental Neural Network (ASOINN) algorithm exploiting a new distance measure and new update rules for handling mixed data. The proposed clustering method is compared with the ASOINN and three other clustering algorithms comprehensively. The results of comparative experiments on various data sets using several clustering evaluation measures show the effectiveness of the proposed mixed data clustering method.