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Three-Way Decisions and Three-Way Clustering

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Rough Sets (IJCRS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11103))

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Abstract

A theory of three-way decisions is formulated based on the notions of three regions and associated actions for processing the three regions. Inspired by the theory of three-way decisions, some researchers have further investigated the theory of three-way decisions and applied it in different domains. After reviewing the recent studies on three-way decisions, this paper introduces the three-way cluster analysis. In order to address the problem of the uncertain relationship between an object and a cluster, a three-way clustering representation is proposed to reflect the three types of relationships between an object and a cluster, namely, belong-to definitely, uncertain and not belong-to definitely. Furthermore, this paper reviews some three-way clustering approaches and discusses some future perspectives and potential research topics based on the three-way cluster analysis.

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Acknowledgements

I am grateful to Professor Yiyu Yao for the discussions. In addition, this work was supported in part by the National Natural Science Foundation of China under grant No. 61533020, 61672120 and 61379114.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Hong Yu

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  1. Hong Yu
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Editors and Affiliations

  1. University of Warsaw, Warsaw, Poland

    Hung Son Nguyen

  2. Faculty of Information Technology, Vietnam National University, Hanoi, Vietnam

    Quang-Thuy Ha

  3. School of Information Science, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  4. Institute of Computer Science, University of Silesia, Sosnowiec, Poland

    Małgorzata Przybyła-Kasperek

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Yu, H. (2018). Three-Way Decisions and Three-Way Clustering. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_2

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