Speedup of interval type 2 fuzzy logic systems based on GPU for robot navigation
Advances in Fuzzy Systems - Special issue on High Performance Fuzzy Systems for Real World Problems
An analog on-line-learning K-means processor employing fully parallel self-converging circuitry
Analog Integrated Circuits and Signal Processing
GPUMAFIA: efficient subspace clustering with MAFIA on GPUs
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
| Hi-index | 0.00 |
The processing power of parallel coprocessors like the Graphics Processing Unit (GPU) is dramatically increasing. However, 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 mesh-based 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.