In order to solve the problem that fcmalgorithm is sensitive to initial clustering center, we use canopyalgorithm to conduct the quick rough clustering. In the meantime, to avoid the blindness of canopyalgorithm, w...
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ISBN:
(纸本)9781509037100
In order to solve the problem that fcmalgorithm is sensitive to initial clustering center, we use canopyalgorithm to conduct the quick rough clustering. In the meantime, to avoid the blindness of canopyalgorithm, we put forward an improved canopy-fcm algorithm based on max-min principle. In allusion to the problem that fcmalgorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved canopy-fcm algorithm. Experimental result shows: improved canopy-fcm algorithm based on MapReduce has better clustering quality and running speed than the canopy-fcm and fcmalgorithm based on MapReduce, and the improved canopy-fcm algorithm based on Hadoop has better speed-up ratio than canopy-fcm based on Standalone mode.
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