IEEE Trans Vis Comput Graph - GPU-based Multilevel Clustering.

Tópicos

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Resumo

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.

Resumo Limpo

process power parallel coprocessor like graphic process unit gpu dramat increas howev now approach present util kind hardwar mesh cluster purpos paper introduc multilevel cluster techniqu design parallel algorithm sole implement gpu formul use spatial coher present cluster optim hierarch cluster merg signific reduc number comparison part approach provid fast high qualiti complet cluster analysi furthermor base origin concept present general method data cluster advantag meshbas techniqu smooth carri general cluster approach addit approach solv problem miss topolog inform inher general data cluster lead local neighbor kmean algorithm evalu techniqu appli centroid voronoi diagram cvd base cluster compar classic approach techniqu generat result least cluster qualiti techniqu prove scale well current limit avail amount graphic memori

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