Evidential clustering of large dissimilarity data
Evidential clustering of large dissimilarity data
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DOI：
10.1016/j.knosys.2016.05.043
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发表时间：
2016-08-15
影响因子：
8.8
通讯作者：
Kanjanatarakul, Orakanya
中科院分区：
文献类型：
--
作者：
Denoeux, Thierry;Sriboonchitta, Songsak;Kanjanatarakul, Orakanya
在证据聚类中，对象的隶属关系被认为是不确定的，并且用Dempster-Shafer质量函数来表示，形成一个凭据划分。EVCLUS算法以这样一种方式构建凭证分区，即对象之间的较大差异对应于相关联的质量函数之间的较高程度的冲突。在本文中，我们提出了对EVCLUS的几点改进，使其适用于非常大的不同数据。首先，EVCLUS算法中基于梯度的优化过程被一种更快的迭代行式二次规划方法所取代。其次，我们证明了EVCLUS可以只提供不同点的随机样本，从而将时间和空间复杂度从二次降低到大致线性。最后，我们介绍了一种两步法来构建信用分区，将质量分配给选定的簇对，使算法输出的信息比原始EVCLU的更多，同时对于大量的簇仍然是可管理的。(C)2016爱思唯尔B.V.保留所有权利。
In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by Dempster-Shafer mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we present several improvements to EVCLUS, making it applicable to very large dissimilarity data. First, the gradient-based optimization procedure in the original EVCLUS algorithm is replaced by a much faster iterative row-wise quadratic programming method. Secondly, we show that EVCLUS can be provided with only a random sample of the dissimilarities, reducing the time and space complexity from quadratic to roughly linear. Finally, we introduce a two-step approach to construct credal partitions assigning masses to selected pairs of clusters, making the algorithm outputs more informative than those of the original EVCLUS, while remaining manageable for large numbers of clusters. (C) 2016 Elsevier B.V. All rights reserved.