2015/07/15 by Kuang Zhou, Zhou, Kuang, Arnaud Martin +6 · 1 citation
Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Data Mining Algorithms and Applications #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1507.04091
in The 18th International Conference on Information Fusion, July 2015, Washington, DC, USA , Jul 2015, Washington, United States
arxiv created 2015/07/15 · openalex publication_date 2015/07/15 · arxiv updated 2015/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.