Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure

Authors
Hongfang Zhou, Yihui Zhang, Yibin Liu
Corresponding Author
Hongfang Zhou
Available Online July 2016.
DOI
https://doi.org/10.2991/iccia-17.2017.67How to use a DOI?
Keywords
Clustering, categorical data, dissimilarity measure, k-modes algorithm.
Abstract
Categorical data clustering has attracted much attentions recently because most practical data contains categorical attributes. The k-modes algorithm, as the extension of the k-means algorithm, is one of the most widely used clustering algorithms for categorical data. In this paper, we firstly analyzed the limitations of two existing dissimilarity measures. Based on this, we proposed a novel dissimilarity measure--IID. IID considers the relationship between the object and all clusters as well as that within clusters. Finally the experiments are made on six benchmark data sets from UCI. And the corresponding results show that IID achieves better performance than two existing ones used in k-modes and KBGRD algorithms.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Hongfang Zhou
AU  - Yihui Zhang
AU  - Yibin Liu
PY  - 2016/07
DA  - 2016/07
TI  - An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 398
EP  - 406
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.67
DO  - https://doi.org/10.2991/iccia-17.2017.67
ID  - Zhou2016/07
ER  -