期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 27, 期 1, 页码 111-123出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2018.2869125
关键词
Belief functions; dempster-shafer theory; density peaks clustering; soft clustering; unsupervised learning
资金
- National Natural Science Foundation of China [51876035, 51676034, 51476028]
- Key Project of Yunnan Power Grid Corporation Ltd. [YNYJ2016043]
This paper introduces a new evidential clustering method based on the notion of belief peaks in the framework of belief functions. The basic idea is that all data objects in the neighborhood of each sample provide pieces of evidence that induce belief on the possibility of such sample to become a cluster center. A sample having higher belief than its neighbors and located far away from the other local maxima is then characterized as cluster center. Finally, a credal partition is created by minimizing an objective function with the fixed cluster centers. An adaptive distance metric is used to fit for unknown shapes of data structures. We show that the proposed evidential clustering procedure has very good performance with an ability to reveal the data structure in the form of a credal partition, from which hard, fuzzy, possibilistic, and rough partitions can be derived. Simulations on synthetic and real-world datasets validate our conclusions.
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