Abstract
AbstractIn this study, we propose a fuzzy clustering algorithm for vectorial data, which is constructed by extending the fuzzification parameters in the q-divergence-based fuzzy c-means algorithm (QFCM). The proposed algorithm, referred to as extended QFCM (eQFCM), is an extension of both QFCM and the penalized fuzzy c-means algorithm proposed by Yang, referred to as Y-type FCM (YFCM). eQFCM extends both the two-parameter QFCM and YFCM algorithms to a four-parameter model. Through numerical experiments using an artificial dataset, we substantiate the theoretical discussion, and the effects of fuzzification parameter to clustering results are observed. Furthermore, the results of some numerical experiments using real datasets are presented to demonstrate that the proposed algorithm outperformed both QFCM and YFCM algorithms in terms of clustering accuracy.
Publisher
Springer Science and Business Media LLC
Reference19 articles.
1. Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press.
2. Cerqueti, R., D’Urso, P., De Giovanni, L., Mattera, R., & Vitale, V. (2023). Fuzzy clustering of financial time series based on volatility spillovers. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05560-7
3. Dinler, D., Tural, M. K., & Ozdemirel, N. E. (2020). Centroid based tree-structured data clustering using vertex/edge overlap and graph edit distance. Annals of Operations Research, 289, 85–122. https://doi.org/10.1007/s10479-019-03505-7
4. Hubert, L., & Arabie, P. (1985). Comparing Partitions. Journal of Classification, 2, 193–218.
5. Ichihashi, H., Honda, K., & Tani, N. (2000). Gaussian mixture PDF approximation and fuzzy $$c$$-means clustering with entropy regularization. In Proceedings of the 4th Asian Fuzzy System Symposium (pp. 217–221).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献