Enhancing Fuzzy C-Means Performance for Low Fuzzifier Values through Ordered Weighted Aggregation
Keywords:
Clustering, Fuzzy C-Means, Ordered Weighted Aggregation Operators, Binomial OWA Operator, Membership, Fuzziness, OrnessAbstract
A prominent fuzzy clustering technique is the Fuzzy C-Means (FCM) in which the fuzzi-fier value m has a substantial effect on the algorithm’s performance. Standard FCM may become unstable in this environment even though smaller values of m result in more refined partitions. Ordered Weighted Aggregation Fuzzy C-Means (OWA-FCM), which integrates an Ordered Weighted Aggregation (OWA) operator into the member-ship update stage, is our approach to address this problem. An orness parameter( p0) controls the binomial distribution which is used to calculate the OWA operator’s weights. For various values of m and p0, extensive experimentation on benchmark datasets were performed. Obtained results clearly indicate that, for m ≳ 2.0, OWA-FCM behaves as regular FCM. On the other hand, OWA-FCM generates more accurate and consistent clustering for lower fuzzifier values (m ≲ 1.8), particularly m ≤ 1.4, leading to better ARI scores and crisper partitions. In addition, throughout a broad range of orness values (0.001 ≤ p0 ≤ 0.999), the final clustering results remain unchanged, suggesting that the OWA-based aggregating mechanism, rather than the particular selection of p0, is accountable for the improvement. These results indicate that when more precise cluster assignments are required, OWA-FCM is a reliable substitute for conventional, standard FCM.
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