Add to ORKGAbstract. Clustering – the grouping of objects depending on their spatial proximity – is one important technique of knowledge discovery in spatial databases. One of the proposed algorithms for this is FDC [5], which uses a density-based clustering approach. Since there is a need for parallel processing in very large databases to distribute resource allocation, this paper presents PFDC, a parallel version of FDC. It has been implemented in C++ using MPI message passing to work on a variety of parallel platforms. Experiments show good speedup results of the proposed scheme
During the last few years, GPUs have evolved from simple devices for the display signal preparation ...
bzhana~hpl.hp.com Data clustering is one of the fundamental techniques in scientific data analysis a...
AbstractWith the advent of Web 2.0, we see a new and differentiated scenario: there is more data tha...
Data clustering is an important data mining technology that plays a crucial role in numerous scienti...
Abstract. To cluster increasingly massive data sets that are common today in data and text mining, w...
Clustering multi-dimensional points is a fundamental task in many fields, and density-based clusteri...
Abstract. In many scientific, engineering or multimedia applications, complex distance functions are...
Abstract: Distributed clustering is an effect method for solving the problem of clustering data loc...
Abstract. The clustering algorithm DBSCAN relies on a density-based notion of clusters and is design...
Abstract—DBSCAN is a widely used isodensity-based clus-tering algorithm for particle data well-known...
Dealing with large samples of unlabeled data is a key challenge in today’s world, especially in appl...
Density-based clustering algorithms are widely used for discovering clusters in pattern recognition ...
Subspace clustering aims to find all clusters in all subspaces of a high-dimensional data space. We ...
In this paper we are going to introduce a new nearest neighbours based approach to clustering, and c...
Clustering analysis is a significant technique in various fields, including unsupervised machine lea...
During the last few years, GPUs have evolved from simple devices for the display signal preparation ...
bzhana~hpl.hp.com Data clustering is one of the fundamental techniques in scientific data analysis a...
AbstractWith the advent of Web 2.0, we see a new and differentiated scenario: there is more data tha...
Data clustering is an important data mining technology that plays a crucial role in numerous scienti...
Abstract. To cluster increasingly massive data sets that are common today in data and text mining, w...
Clustering multi-dimensional points is a fundamental task in many fields, and density-based clusteri...
Abstract. In many scientific, engineering or multimedia applications, complex distance functions are...
Abstract: Distributed clustering is an effect method for solving the problem of clustering data loc...
Abstract. The clustering algorithm DBSCAN relies on a density-based notion of clusters and is design...
Abstract—DBSCAN is a widely used isodensity-based clus-tering algorithm for particle data well-known...
Dealing with large samples of unlabeled data is a key challenge in today’s world, especially in appl...
Density-based clustering algorithms are widely used for discovering clusters in pattern recognition ...
Subspace clustering aims to find all clusters in all subspaces of a high-dimensional data space. We ...
In this paper we are going to introduce a new nearest neighbours based approach to clustering, and c...
Clustering analysis is a significant technique in various fields, including unsupervised machine lea...
During the last few years, GPUs have evolved from simple devices for the display signal preparation ...
bzhana~hpl.hp.com Data clustering is one of the fundamental techniques in scientific data analysis a...
AbstractWith the advent of Web 2.0, we see a new and differentiated scenario: there is more data tha...