caa a22 4500 605469555 CHVBK 20210128100323.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1296-7 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1296-7 An incremental mixed data clustering method using a new distance measure [Elektronische Daten] [Fakhroddin Noorbehbahani, Sayyed Mousavi, Abdolreza Mirzaei] 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. Springer-Verlag Berlin Heidelberg, 2014 Mixed data clustering nationallicence Incremental learning nationallicence Distance measure nationallicence Clustering evaluation measures nationallicence SOM nationallicence Noorbehbahani Fakhroddin Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran aut Mousavi Sayyed Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran aut Mirzaei Abdolreza Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 731-743 1432-7643 19:3<731 2015 19 500 https://doi.org/10.1007/s00500-014-1296-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1296-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Noorbehbahani Fakhroddin Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran aut NATIONALLICENCE 700 1- Mousavi Sayyed Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran aut NATIONALLICENCE 700 1- Mirzaei Abdolreza Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 731-743 1432-7643 19:3<731 2015 19 500