A Survey on Interpretable Clustering

Haoyu Yang, Lianmeng Jiao, Quan Pan

科研成果: 书/报告/会议事项章节会议稿件同行评审

7 引用 (Scopus)

摘要

Clustering is the process of dividing a collection of physical or abstract objects into several classes composed of similar objects. Now there are many clustering algorithms with superior performance, but the clusters generated by them are difficult for human to understand. Thus, some interpretable clustering methods are proposed, which make the clustering results have good interpretability without much impact on the clustering accuracy. This paper reviews the interpretable clustering algorithms, introduces and summarizes the previous work in this field according to the different interpretative ways, including rules, rectangular bounds and decision trees, and explores the development of interpretable clustering algorithms in the future.

源语言英语
主期刊名Proceedings of the 40th Chinese Control Conference, CCC 2021
编辑Chen Peng, Jian Sun
出版商IEEE Computer Society
7384-7388
页数5
ISBN(电子版)9789881563804
DOI
出版状态已出版 - 26 7月 2021
活动40th Chinese Control Conference, CCC 2021 - Shanghai, 中国
期限: 26 7月 202128 7月 2021

出版系列

姓名Chinese Control Conference, CCC
2021-July
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议40th Chinese Control Conference, CCC 2021
国家/地区中国
Shanghai
时期26/07/2128/07/21

指纹

探究 'A Survey on Interpretable Clustering' 的科研主题。它们共同构成独一无二的指纹。

引用此