This paper sets a model for Distributed Valued Constraint Satisfaction Problems, and proposes an incompletemethod for solving such problems. This method is a greedy repair distributed algorithm which extends to the distributed case any greedy repair centralized algorithm. Experiments are carried out on a real-world problem and show the practical interest of this method.

In this paper we present two new approaches for finding good starting solutions to the planar p-median problem. Both methods rely on a discrete approximation of continuous model that restricts facility locations given set demand points. The first method adapts phase greedy random construction algorithm proposed minimum sum squares clustering second one implements simple descent procedure based ...

Functional data, where samples are random functions, are increasingly common and important in a variety of applications, such as health care and traffic analysis. They are naturally high dimensional and lie along complex manifolds. These properties warrant use of the subspace assumption, but most state-of-the-art subspace learning algorithms are limited to linear or other simple settings. To ad...

We investigate the geometric properties of the communication graph in realistic low-power wireless networks. In particular, we explore the concept of the curvature of a wireless network via the clustering coefficient. Clustering coefficient analysis is a computationally simplified, semilocal approach, which nevertheless captures such a large-scale feature as congestion in the underlying network...

Cluster validity has been mainly used to evaluate the quality of individual clusters, and compare whole partitions resulting from different or same (using different parameters) clustering algorithms [13]. However, depending on the application, the demands for a validity measure may differ, inducing the necessity of introducing new measures which will suit to the problem under investigation. We ...

Many real-world networks have high clustering among vertices: vertices that share neighbors are often also directly connected to each other. A network’s clustering can be a useful indicator of its connectedness and community structure. Algorithms for generating networks with high clustering have been developed, but typically rely on adding or removing edges and nodes, sometimes from a completel...

The Correlation Clustering problem has been introduced recently [N. Bansal, A. Blum, S. Chawla, Correlation Clustering, in: Proc. 43rd Symp. Foundations of Computer Science, FOCS, 2002, pp. 238–247] as a model for clustering data when a binary relationship between data points is known. More precisely, for each pair of points we have two scores measuring the similarity and dissimilarity respecti...

Biclustering is a two way clustering approach involving simultaneous clustering along two dimensions of the data matrix. Finding biclusters of web objects (i.e. web users and web pages) is an emerging topic in the context of web usage mining. It overcomes the problem associated with traditional clustering methods by allowing automatic discovery of browsing pattern based on a subset of attribute...