Vc-means: um novo algoritmo de agrupamento

Abstract

This study presents the development and evaluation of the Vc-Means algorithm as an innovative approach to data clustering. The Vector c-Means (Vc-Means) is based on a previously developed algorithm called CK-Means and is designed to identify patterns and specific clusters in data sets. Statistical tests were conducted on 20 traditional data sets, comparing and validating its efficiency against three well-known algorithms in the literature: K-Means, Fuzzy C-Means (FCM), and Gustafson-Kessel (GK). The evaluation was performed using validation indices such as the DB index, Silhouette, Adjusted Rand Index, Calinski-Harabasz, Adjusted Mutual Information, and V-measure. The results showed that Vc-Means achieved great performance, with no significant statistical difference compared to the other algorithms, and demonstrated remarkable efficiency in terms of processing time.