Open AccessConditional semi‐fuzzy c ‐means clustering for imbalanced dataset
Author(s) -
Gao Yunlong,
Yang Chengyu,
Lin KuoYi,
Pan Jinyan,
Li Li
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0253
Subject(s) - fuzzy logic , partition (number theory) , pattern recognition (psychology) , data mining , fuzzy clustering , benchmark (surveying) , cluster analysis , computer science , fuzzy set , artificial intelligence , mathematics , boundary (topology) , algorithm , geodesy , combinatorics , geography , mathematical analysis
Fuzzy c‐means algorithms have been widely utilised in several areas such as image segmentation, pattern recognition and data mining. However, the related studies showed the limitations in facing imbalanced datasets. The maximum fuzzy boundary tends to be located on the largest cluster which is not desirable. The overall fuzzy partition results in false grouping of edge objects and weakens the compactness of cluster. It is important the clusters are delineated by the maximum fuzzy boundary. In this study, a semi‐fuzzy c‐means algorithm is proposed by combining hard partition and soft partition. This study aims to provide an effective partition for the edge objects, such that the compactness of cluster can be improved. The proposed algorithm integrates the semi‐fuzzy c‐means method with the size‐insensitive integrity‐based fuzzy c‐means algorithm. In particular, the latter algorithm has the ability to deal with imbalanced data. With the experiment validation, the proposed algorithm is robust and outperforms the two component algorithms by using synthetic and widely known benchmark datasets.