Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago. Since then, continuous efforts have been taken to enhance its performance. Unfortunately, a good trade-off between quality and efficiency is hardly reached. In this paper, a novel k-means variant is presented. Different from most of k-means variants, the clustering procedure is explicitly dri...

In this article, the anti-plane deformation of an orthotropic sector with multiple defects is studied analytically. The solution of a Volterra-type screw dislocation problem in a sector is first obtained by means of a finite Fourier cosine transform. The closed form solution is then derived for displacement and stress fields over the sector domain. Next, the distributed dislocation method is em...

Over the last years, many variations of the quadratic k-means clustering procedure have been proposed, all aiming to robustify the performance of the algorithm in the presence of outliers. In general terms, two main approaches have been developed: one based on penalized regularization methods, and one based on trimming functions. In this work, we present a theoretical analysis of the robustness...

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search pro...

Kernel -means is an extension of the standard -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel -means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedu...

Different types of decentralized clustering problems have been studied so far for networks and multi-agent systems. In this paper we introduce a new type of a decentralized clustering problem for networks. The so called Decentralized Packet Clustering (DPC) problem is to find for packets that are sent around in a network a clustering. This clustering has to be done by the routers using only few...

well as in other applications such as bioinformatics, we are interested in an angle between document vectors, hence, it is convenient to consider sets of normalized vectors. A wide variety of clustering algorithms applicable for objects of general nature exist,1 of these, the k-means algorithm might be the most widely accepted general clustering technique. In this article we suggest a new two-s...