We present an algorithm for cluster dynamics to efficiently simulate large systems on MIMD parallel computers with large numbers of processing nodes. The method divides physical space into rectangular cells which are assigned to processing nodes and combines a serial procedure, by which clusters

Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance

Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance

In this article, we would like to introduce our approach to cluster labeling with Linked Data. Clustering web pages into semantically related groups promises better performance in searching the Web. Nowadays, only special semantic search engines provide clustering of results. Other engines

The purpose of text clustering in information retrieval is to discover groups of semantically related documents. Accurate and comprehensible cluster descriptions (labels) let the user comprehend the collection’s content faster and are essential for various document browsing interfaces. The task

ABSTRACT Cluster label quality is crucial for browsing topic hierarchies obtained via document clustering. Intuitively, the hierarchical structure should influence the labeling accuracy. However, most labeling algorithms ignore such structural properties and therefore, the impact of hierarchical

This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which

applications (that generally do not require large amounts of communication), EcliPSe can be used for more general forms of message-passing parallel processing. We describe the use of the toolkit in the parallelization of a cluster labeling algorithm. The algorithm is designed so that it uses some of Ecli

in clustering by studying a labeled block model, where in addition to the edge information, the true cluster labels of a small fraction of the nodes are revealed. In the case of two clusters, we show that below the threshold, a small amount of node information does not affect recovery. On the other hand, we

Cluster algorithms are non-local Monte Carlo update schemes which can greatly increase the efficiency of computer simulations of spin models of magnets. The major computational task in these algorithms is connected component labeling, to identify clusters of connected sites on a lattice. We have