There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

DBSCAN is a well-known density based clustering algorithm capable of discovering
arbitrary shaped clusters and eliminating noise data. However, parallelization of DBSCAN is …

This paper proposes a computationally efficient algorithm for distributed fusion in a sensor
network in which multi-Bernoulli (MB) filters are locally running in every sensor node for …

Connected components is a fundamental kernel in graph applications. The fastest existing
parallel multicore algorithms for connectivity are based on some form of edge sampling …

Graph connectivity is a fundamental problem in computer science with many important
applications. Sequentially, connectivity can be done in linear work easily using breadth-first …

Dbscan is a density-based clustering algorithm which is well known for its ability to discover
clusters of arbitrary shape as well as to distinguish noise. As it is computationally expensive …

OPTICS is a hierarchical density-based data clustering algorithm that discovers arbitrary-
shaped clusters and eliminates noise using adjustable reachability distance thresholds …

Connected Components is a fundamental graph mining problem that has been studied for
the PRAM, MapReduce and BSP models. We present a simple CC algorithm for BSP that …

We present an efficient distributed memory parallel algorithm for computing connected
components in undirected graphs based on Shiloach-Vishkin's PRAM approach. We discuss …

DBSCAN is a widely used is density-based clustering algorithm for particle data well-known
for its ability to isolate arbitrarily-shaped clusters and to filter noise data. The algorithm is …