Adaptive Three-Way C-Means Clustering Based on the Cognition of Distance Stability

Soft clustering can be regarded as a cognitive computing method that seeks to deal with the clustering with fuzzy boundary. As a classical soft clustering algorithm, rough k-means (RKM) has yielded various extensions. However, some challenges remain in existing RKM extensions. On the one hand, the user-defined cutoff threshold is subjective and cannot be changed during iteration. On the other hand, the weight of the object to the cluster center is calculated by membership grade and a subjective parameter, that is, the fuzzifier, which complicates the issue and reduces the robustness of the algorithm. In this paper, inspired by human cognition of distance stability, an adaptive three-way c-means algorithm is proposed. First, in human cognition, objects are clustered according to the stability of their distance to the clusters, and variance is an effective way to measure the stability of data. Based on this, an adaptive cutoff threshold is introduced by determining the maximum increment between the variances of distance. Second, based on the cognition that distance is inversely proportional to weight, the weight equation is defined by distance without introducing any subjective parameters. Then, combined with the adaptive cutoff threshold and weight equation, A-3WCM is proposed. The experimental results show that A-3WCM exhibits excellent performance and outperforms five representative algorithms related to RKM on nine popular datasets.

Automated summary

This paper proposes a new adaptive three-way c-means clustering algorithm, which addresses some challenges in existing RKM extensions by introducing an adaptive cutoff threshold and a weight equation without subjective parameters. Experimental results demonstrate that A-3WCM exhibits excellent performance on nine popular datasets.