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Fuzzy Separation And Shrinkage Clustering

Authors :
Ning Yu
Haoran Liang
Qingqiang Wu
Kunhong Liu
Yanxiang Zong
Heying Zhu
Source :
Journal of Physics: Conference Series. 1693:012095
Publication Year :
2020
Publisher :
IOP Publishing, 2020.

Abstract

Clustering has many applications in data mining and machine learning. Fuzzy clustering methods have been widely used in clustering. However, fuzzy clustering methods still have a fatal problem: the cluster radius sensitivity problem. The cluster radius sensitivity problem means that clusters with smaller radius will predominate in clustering and obtain more data points. Aiming at this problem, we propose a fuzzy separation and shrinkage clustering algorithm (FSC). FSC uses cluster membership degrees and cluster sizes to construct a new membership distribution, and then moves the data points according to this new membership distribution. The accuracies of our algorithm on wine, iris, balance scale and seeds are as follows: 98.82%, 97.27%, 63.07% and 91.34%. Our contributions are: (1) We propose a fuzzy separation and shrinkage clustering algorithm, which can solve the cluster radius sensitivity problem. (2) The performance of our algorithm on the UCI datasets goes beyond the benchmark algorithms.

Details

ISSN :
17426596 and 17426588
Volume :
1693
Database :
OpenAIRE
Journal :
Journal of Physics: Conference Series
Accession number :
edsair.doi...........43362946a6d9418e31f2eb4bff571e5b
Full Text :
https://doi.org/10.1088/1742-6596/1693/1/012095