@inproceedings{cecd92b0f8ec45b5b1f827f6e5e87448,
title = "Fast Unfolding of Credal Partitions in Evidential Clustering",
abstract = "Evidential clustering, based on the notion of credal partition, has been successfully applied in many fields, reflecting its broad appeal and usefulness as one of the steps in exploratory data analysis. However, it is time-consuming due to the introduction of meta-cluster, which is regarded as a new cluster and defined by the disjunction (union) of several special (singleton) clusters. In this paper, a simple and fast method is proposed to extract the credal partition structure in evidential clustering based on modifying the iteration rule. By doing so, the invalid computation associated with meta-clusters is effectively eliminated. It is superior to known methods in terms of execution time. The results show the potential of the proposed method, especially in large data.",
keywords = "Belief functions, Evidential clustering, Fast credal partition, Uncertainty",
author = "Zuowei Zhang and Arnaud Martin and Zhunga Liu and Kuang Zhou and Yiru Zhang",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 6th International Conference on Belief Functions, BELIEF 2021 ; Conference date: 15-10-2021 Through 19-10-2021",
year = "2021",
doi = "10.1007/978-3-030-88601-1_1",
language = "英语",
isbn = "9783030886004",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--12",
editor = "Thierry Den{\oe}ux and Eric Lef{\`e}vre and Zhunga Liu and Fr{\'e}d{\'e}ric Pichon",
booktitle = "Belief Functions",
}