Open AccessAn Interval Type-2 Possibilistic C-Means Clustering Algorithm and Its Application
Author(s) -
Haihua Xing,
Huannan Chen,
Hui Lin,
Xinghui Wu
Publication year - 2021
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 - 10.1088/1742-6596/2132/1/012016
Subject(s) - cluster analysis , interval (graph theory) , noise (video) , segmentation , algorithm , fuzzy logic , fuzzy clustering , type (biology) , pattern recognition (psychology) , image segmentation , fuzzy set , computer science , mathematics , data mining , artificial intelligence , image (mathematics) , ecology , combinatorics , biology
In this paper, we aim at the fuzzy uncertainty caused by noise in pattern data. The advantages of PCM algorithm to deal with noise and interval type-2 fuzzy sets to deal with high-order uncertainties are used, respectively. An interval type-2 probability C-means clustering (IT2-PCM) based on penalty factor is proposed. The performance of the algorithm is evaluated by two sets of data sets and two groups of images segmentation experiments. The results show that IT2-PCM algorithm can assign proper membership degrees to clustering samples with noise, and it can detect noise points effectively, and it has good performance in image segmentation.