An axiomatic framework for three-way clustering

被引:5
|
作者
Chen, Yingxiao [1 ,2 ]
Zhu, Ping [1 ,3 ]
Yao, Yiyu [2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
[2] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
[3] Beijing Univ Posts & Telecommun, Key Lab Math & Informat Networks, Minist Educ, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Three-way clustering; Three-way decision; Axiomatic framework; FUZZY;
D O I
10.1016/j.ins.2024.120761
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Three-way clustering provides a variety of models with richer structural features than traditional two-way clustering. However, the structural properties of three-way clustering have not been systematically studied. In this paper, we propose an axiomatic framework to study three-way clustering based on the structural properties of three-way clusters. We categorize three-way clustering models into 16 types organized in five levels according to six axioms. We propose three strategies for three-way clustering approaches: the 2to3WC Strategy, the I3WC Strategy, and the E3WC Strategy. These strategies comprehensively cover existing three-way clustering models. We examine each of the existing methods and incorporate almost all of them into these three strategies. The framework not only summarizes the structural properties and methods of existing studies but also provides inspiration for future research. There are six types of three-way clustering models that have not yet been proposed and require further study. Strategies that have not been applied to each type also suggest possible future research directions.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] A three-way clustering approach using image enhancement operations
    Ali, Bahar
    Azam, Nouman
    Yao, JingTao
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2022, 149 : 1 - 38
  • [42] M3W: Multistep Three-Way Clustering
    Du, Mingjing
    Zhao, Jingqi
    Sun, Jiarui
    Dong, Yongquan
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (04) : 5627 - 5640
  • [43] An automatic three-way clustering method based on sample similarity
    Xiuyi Jia
    Ya Rao
    Weiwei Li
    Sichun Yang
    Hong Yu
    International Journal of Machine Learning and Cybernetics, 2021, 12 : 1545 - 1556
  • [44] A three-way clustering method based on an improved DBSCAN algorithm
    Yu, Hui
    Chen, LuYuan
    Yao, JingTao
    Wang, XingNan
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 535
  • [45] An automatic three-way clustering method based on sample similarity
    Jia, Xiuyi
    Rao, Ya
    Li, Weiwei
    Yang, Sichun
    Yu, Hong
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (05) : 1545 - 1556
  • [46] The three-way
    Siegel, CL
    ANNALS OF MATHEMATICS, 1944, 42 : 127 - 168
  • [47] An efficient three-way clustering algorithm based on gravitational search
    Yu, Hong
    Chang, Zhihua
    Wang, Guoyin
    Chen, Xiaofang
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2020, 11 (05) : 1003 - 1016
  • [48] Least-squares bilinear clustering of three-way data
    Schoonees, Pieter C.
    Groenen, Patrick J. F.
    van de Velden, Michel
    ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2022, 16 (04) : 1001 - 1037
  • [49] Distance models for three-way tables and three-way association
    de Rooij, M
    JOURNAL OF CLASSIFICATION, 2002, 19 (01) : 161 - 178
  • [50] Distance Models for Three-Way Tables and Three-Way Association
    Mark de Rooij
    Journal of Classification, 2002, 19 : 161 - 178