This paper presents a novel accelerated exact k-means called as" Ball k-means" by using
the ball to describe each cluster, which focus on reducing the point-centroid distance …

This paper presents a novel accelerated exact-means called as “Ball-means” by using the
ball to describe each cluster, which focus on reducing the point-centroid distance …

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm that effectively
expresses complex real world information by integrating fuzzy parameters. Due to its …

Clustering is one of the main methods for getting insight on the underlying nature and
structure of data. The purpose of clustering is organizing a set of data into clusters, such that …

An improved version of K‐means clustering algorithm that can be applied to big data
through lower processing loads with acceptable precision rates is presented here. In this …

In this paper we propose a criterion to balance the processing time and the solution quality
of k-means cluster algorithms when applied to instances where the number n of objects is …

This paper proposes a new criterion for reducing the processing time of the assignment of
data points to clusters for algorithms of the k-means family, when they are applied to …

In this paper, an enhanced version of the K-Means algorithm that incorporates a new
convergence criterion is presented. The largest centroid displacement at each iteration was …

The conventional K-medoids algorithm is one of the most used clustering algorithms,
however, one of its limitations is its sensitivity to initial medoids. The generation of optimized …

Con la presencia cada vez mayor de Big Data surge la necesidad de agrupar grandes
instancias. Estas instancias presentan un número de objetos de naturaleza …