摘要： Coppi et al. [ 7 ] applied Yang and Wu's [ 20 ] idea to propose a possibilistic k-means (PkM) clustering algorithm for LR-type fuzzy numbers. The memberships in the objective function of PkM no longer need to satisfy the constraint in fuzzy k-means that of a data point across classes sum to one. However, the clustering performance of PkM depends on the initializations and weighting exponent. In this paper, we propose a robust clustering method based on a self-updating procedure. The proposed algorithm not only solves the initialization problems but also obtains a good clustering result. Several numerical examples also demonstrate the effectiveness and accuracy of the proposed clustering method, especially the robustness to initial values and noise. Finally, three real fuzzy data sets are used to illustrate the superiority of this proposed algorithm. 出版者： Abingdon: Taylor & Francis 出版日期： 2015-07-03 出處： Journal of applied statistics, 2015-07, Vol.42 (7), p.1503-1518 資源來源： EBSCOhost Business Source Premier 版權： 2015 Taylor & Francis 2015 版權： Copyright Taylor & Francis Ltd. 2015 識別號： ISSN: 0266-4763 識別號： EISSN: 1360-0532 識別號： DOI: 10.1080/02664763.2014.1001326
