CAJ | 학술논문

采取了3种必要的措施提高了聚类质量：考虑到各维数据特征属性对聚类效果影响不同，采用了基于统计方法的维度加权的方法进行特征选择；对于和声搜索算法的调音概率进行了改进，将改进的和声搜索算法和模糊聚类相结合用于快速寻找最优的聚类中心；循环测试各种中心数情况下的聚类质量以获得最佳的类中心数。该算法被应用于并行计算性能分析中，用于识别并行程序运行时各处理器运行性能瓶颈的类别。实验结果表明该算法较其他算法更优，这样的性能分析方法可以提高并行程序的运行效率。
채취료3충필요적조시제고료취류질량：고필도각유수거특정속성대취류효과영향불동，채용료기우통계방법적유도가권적방법진행특정선택；대우화성수색산법적조음개솔진행료개진，장개진적화성수색산법화모호취류상결합용우쾌속심조최우적취류중심；순배측시각충중심수정황하적취류질량이획득최가적류중심수。해산법피응용우병행계산성능분석중，용우식별병행정서운행시각처리기운행성능병경적유별。실험결과표명해산법교기타산법경우，저양적성능분석방법가이제고병행정서적운행효솔。
Three methods are adopted to achieve a better clustering quality. Considering the different influences of each dimen-sion attribute of data on the clustering effect, statistic method is used to weight each dimension to select feature. Some improve-ments 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 per-formance bottleneck category of various processors during parallel program running. Experimental results show that the pro-posed clustering algorithm outperforms other similar algorithms. This performance analysis method can improve the operating efficiency of the parallel program.