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Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm
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DOI：10.1109/ACCESS.2022.3155869.png)
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摘要
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Atanassov intuitionistic fuzzy set (AIFS) has the capability;
deal with various uncertain situations, so its popularity among researchers is quite high. It has been observed that Euclidean distance measure based AIFS clustering algorithms perform well on imprecise datasets. The performance of Euclidean measure based clustering algorithms deteriorates due;
the presence of outliers/noise within a dataset. In the paper, an extension of the algorithm given by Leski, Jacek M. [Fuzzy Sets and Systems, 286 (2016): 114-133] is proposed as intuitionistic fuzzy c-ordered means clustering algorithm. This paper analyses the functionality of clustering algorithms over outliers/noises based datasets. In IFCOM, an alternate of Euclidean distance known as Loss function is used. Moreover, IFCOM uses intuitionistic fuzzy OWA;
combat the ill effects of the noises and outliers. The proposed algorithm exploits a typicality function based weighing ordering approach. The approach assigins lower weights;
the outliers. Hence, the catastrophic behavior is not observed in IFCOM while dealing outliers possessing synthetic and UCI machine learning datasets.
Keyword:
Fuzzy clustering
intuitionistic fuzzy sets
IFCM
loss functions
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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