摘要
针对模糊C-均值聚类算法容易陷入局部极值等缺陷,提出了基于改进QPSO的模糊C-均值聚类,算法利用QPSO的优点,并对量子门更新策略进行了改进。实验结果显示该算法提高了模糊聚类算法的聚类效果以及搜索能力,在全局寻优能力、跳出局部最优能力、收敛速度等方面具有优势。
Since the fuzzy C-means clustering algorithm is easy to fall into local extremum,fuzzy C-means clustering algo-rithm based on the improved quantum particle swarm optimization （QPSO） is proposed. The local search ability and quantum gates update strategy were improved by making full use of the advantages of fast convergence of QPSO. The experimental results show that the algorithm improves the search ability and clustering effect of fuzzy clustering algorithm,and has superiority in the aspects of global optimization capability,jumping out of local optimum capacity and convergence rate.
出处
《现代电子技术》
2014年第7期118-120,共3页
Modern Electronics Technique
基金
河南省科技计划重点项目资助(102102210416)
关键词
模糊C-均值聚类
量子粒子群优化
聚类分析
量子门更新策略
fuzzy C-means clustering
quantum particle swarm optimization
clustering analysis
quantum gates update strategy
