关键词: 和声搜索, 模糊聚类, 特征选择, 并行性能分析

Abstract: Three methods are adopted to achieve a better clustering quality. Considering the different influences of each dimension attribute of data on the clustering effect, statistic method is used to weight each dimension to select feature. Some improvements are carried out for the probability of harmony search algorithm and combine fuzzy clustering algorithm with harmony search to rapidly find the optimal cluster centers. The iterative method is used to test clustering quality to get the best number of cluster center. The proposed algorithm is applied to parallel computing performance analysis to distinguish and identify the performance bottleneck category of various processors during parallel program running. Experimental results show that the proposed clustering algorithm outperforms other similar algorithms. This performance analysis method can improve the operating efficiency of the parallel program.

Key words: harmony search, fuzzy clustering, feature selection, parallel performance analysis