This paper presents two fuzzy clustering algorithms for categorical multivariate data based on qdivergence. First, this study shows that a conventional method for vectorial data can be explained as regularizing another conventional method using qdivergence. Second, based on the known results that Kullback-Leibler (KL)-divergence is generalized into the q-divergence, and two conventional fuzzy clustering methods for categorical multivariate data adopt KL-divergence, two fuzzy clustering algorithms for categorical multivariate data that are based on qdivergence are derived from two optimization problems built by extending the KL-divergence in these conventional methods to the q-divergence. Through numerical experiments using real datasets, the proposed methods outperform the conventional methods in term of clustering accuracy.
|ジャーナル||Journal of Advanced Computational Intelligence and Intelligent Informatics|
|出版物ステータス||Published - 2018 7 1|
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence