Abstract

Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm used successfully in many real-world applications. The strengths of RFSVC are its ability to handle arbitrary cluster shapes, identify the number of clusters, and effectively detect outliers by using the membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers' membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing final membership degrees and, at the same time, allows a better interpretation of the cluster structures found. Particularly, we propose the OWA using weights computed by the linguistic and exponential quantifiers. The computational experiments show that our approach obtains comparable results with the current version of RFSVC. However, the former weights all clusters' support vectors in the computation of membership degrees while maintaining their interpretability level for detecting outliers.