Open AccessFuzzy Separation And Shrinkage Clustering
Author(s) -
Ning Yu,
Yanxiang Zong,
Haoran Liang,
Heying Zhu,
Kun-Hong Liu,
Qingqiang Wu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012095
Subject(s) - cluster analysis , fuzzy clustering , correlation clustering , k medians clustering , cure data clustering algorithm , data mining , pattern recognition (psychology) , canopy clustering algorithm , single linkage clustering , flame clustering , fuzzy logic , benchmark (surveying) , computer science , mathematics , artificial intelligence , cluster (spacecraft) , clustering high dimensional data , determining the number of clusters in a data set , geography , geodesy , programming language
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.