计算机科学 ›› 2016, Vol. 43 ›› Issue (3): 256-261.doi: 10.11896/j.issn.1002-137X.2016.03.047
张恒巍,何嘉婧,韩继红,王晋东
ZHANG Heng-wei, HE Jia-jing, HAN Ji-hong and WANG Jin-dong
摘要: 为解决选定特征上的聚类问题和模糊C-均值聚类存在的初始值敏感、易陷入局部最优的问题,提出了一种基于改进萤火虫算法的模糊软子空间聚类方法。该方法在模糊C-均值聚类算法的基础上,采用基于数据可靠性的k-均值算法中特征权值的计算方法,并结合萤火虫算法的全局搜索能力对所有的特征子空间进行搜索;设计了一种目标函数来对聚类结果和子空间所包含的特征维进行评估,并利用目标函数改进了萤火虫算法的搜索公式。实验结果表明,该方法能有效地收敛于全局最优解,具有良好的聚类效果和抗噪性。
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