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Outranking-based approaches for multiple criteria partially ordered clustering: A review of existing algorithms, new proposals, and experimental comparison


[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doctoral school student | [ S ] student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in

Information Sciences

Journal year: 2024 | Journal volume: vol. 678

Article type

scientific article

Publication language


  • Partially ordered clustering
  • Multiple criteria decision aiding
  • Outranking relation
  • Experimental comparison

EN We consider clustering problems that involve categorizing alternatives into partially ordered, initially undefined groups based on their performance across multiple criteria. To achieve this, we use an outranking relation model to reflect the Decision Maker's preferences. We examine various algorithms that not only group the alternatives but also order the clusters in different ways. This analysis includes innovative approaches that use distances in the space of outranking relations or detailed relation profiles, and apply orthogonal non-negative factorization to outranking matrices. Additionally, we discuss a set of measures, including two novel ones, for assessing the effectiveness of clustering when the groupings are partially ordered. Our findings are based on comprehensive computational experiments on real-world and simulated datasets. Beyond evaluating various methods using four quality metrics and computational efficiency, we explore the influence of accessible preference structures and ordering techniques on the clustering outcomes.

Date of online publication


Pages (from - to)

121014-1 - 121014-26





Article Number: 121014

Ministry points / journal


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