摘要： 在基于模糊C-均值(FCM)的不确定区间数聚类算法中,区间数内的点通常被假设服从均匀分布而难以表达其真实属性,聚类结果受初始聚类中心影响较大且隶属度更新速度较慢。为此,提出一种基于一般分布区间数的不确定高效区间数模糊(EFCM-ID)聚类算法。基于四分位数思想设计适用于一般分布区间数的距离度量——MQ距离,准确刻画不确定数据。结合密度思想和随机抽样策略提出初始聚类中心的优化选取方法SDCS,提升算法精度。在此基础上,利用竞争学习思想构建相对加速隶属度更新策略,减少算法的运行时间。实验结果表明,与YFCM、XFCM和ExpFCMd-ID算法相比,该算法具有较好的稳定性,并且聚类效率更高。

关键词: 不确定聚类, 区间数, 模糊C-均值, 密度思想, 竞争学习思想

Abstract: The uncertain interval number clustering algorithm based on Fuzzy C-Means (FCM) has the problem that the point in the interval number is usually assumed to be the uniform distribution and difficult to express its real attribute,the clustering results are greatly affected by the initial clustering center and the updating speed of membership degree is slow.Therefore,an uncertain Efficient Fuzzy C-Means for Interval-valued Data(EFCM-ID) clustering algorithm based on general distributed interval number is proposed.Based on the idea of quartile,the distance metric MQ distance for general distributed interval numbers is designed,and the uncertain data are accurately described.Combining the density idea and random sampling strategy,an optimal selection method SDCS of initial clustering center is proposed to improve the accuracy of the algorithm,and then a relative accelerated membership updating strategy is constructed by using the competitive learning theory,which reduces the running time of the algorithm.Experimental results show that,compared with YFCM,XFCM and ExpFCMd-ID algorithm,this algorithm has better stability and higher clustering efficiency.

Key words: uncertain clustering, interval number, Fuzzy C-Means (FCM), density thought, competitive learning thought

中图分类号: 

• TP301.6