Name
Abstract | ||
|---|---|---|
The processing power of parallel co-processors like the Graphics Processing Unit (GPU) are dramatically increasing. However, up until now only a few approaches have been presented to utilize this kind of hardware for mesh clustering purposes. In this paper we introduce a Multilevel clustering technique designed as a parallel algorithm and solely implemented on the GPU. Our formulation uses the spatial coherence present in the cluster optimization and hierarchical cluster merging to significantly reduce the number of comparisons in both parts . Our approach provides a fast, high quality and complete clustering analysis. Furthermore, based on the original concept we present a generalization of the method to data clustering. All advantages of the meshbased techniques smoothly carry over to the generalized clustering approach. Additionally, this approach solves the problem of the missing topological information inherent to general data clustering and leads to a Local Neighbors k-means algorithm. We evaluate both techniques by applying them to Centroidal Voronoi Diagram (CVD) based clustering. Compared to classical approaches, our techniques generate results with at least the same clustering quality. Our technique proves to scale very well, currently being limited only by the available amount of graphics memory. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TVCG.2010.55 | Visualization and Computer Graphics, IEEE Transactions |
Keywords | Field | DocType |
computational geometry,computer graphic equipment,coprocessors,optimisation,parallel algorithms,pattern clustering,statistical analysis,Centroidal Voronoi Diagram,GPU-based multilevel clustering,cluster optimization,clustering analysis,generalization,graphics memory,graphics processing unit,hierarchical cluster,local neighbors k-means algorithm,mesh clustering,parallel algorithm,parallel coprocessors,spatial coherence,Computer graphics,clustering methods,hierarchical methods,parallel processing,programmable graphics hardware. | Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Computer science,Theoretical computer science,Constrained clustering,Cluster analysis | Journal |
Volume | Issue | ISSN |
17 | 2 | 1077-2626 |
Citations | PageRank | References |
6 | 0.47 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iurie Chiosa | 1 | 9 | 0.87 |
| Andreas Kolb | 2 | 783 | 71.76 |