Adaptive Resonance Theory-based Clustering for Handling Mixed Data

Naoki Masuyama1; Yusuke Nojima1; Hisao Ishibuchi2; Zongying Liu3

10.1109/IJCNN55064.2022.9892060

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

2161-4393

978-1-6654-9526-4

2022 International Joint Conference on Neural Networks (IJCNN)

1-8

18-23 July 2022

Padua, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

This paper proposes an Adaptive Resonance Theory (ART)-based clustering algorithm for a dataset which contains numerical and categorical attributes simultaneously. In the proposed algorithm, similarity between numerical attributes is calculated by the correntropy-based nonlinear similarity measurement, while similarity between categorical attributes is defined by a hamming distance-based approach. One advantage of the proposed algorithm is that the algorithm continually and adaptively generates a sufficient number of nodes for clustering from given data points. Empirical studies on various datasets show that the proposed algorithm has comparable clustering performance to the representative mixed data clustering algorithms.

Adaptive Resonance Theory Clustering Mixed Data Correntropy

其他

英语

CPCI-S

National Natural Science Foundation of China[61876075]

Computer Science ; Engineering ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Neurosciences

WOS:000867070901070

IEEE

1.Department of Core Informatics, Osaka Metropolitan University, Osaka, Japan
2.Shenzhen Key Laboratory of Computational Intelligence, Southern University of Science and Technology, Shenzhen, China
3.Navigation College, Dalian Maritime University, Dalian, China

Naoki Masuyama,Yusuke Nojima,Hisao Ishibuchi,et al. Adaptive Resonance Theory-based Clustering for Handling Mixed Data[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-8.