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A Multicriteria Clustering Approach Based on Similarity Indices and Clustering Ensemble Techniques

Author

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  • Baroudi Rouba

    (Department of Computer Science, Université de Mostaganem Abelhamid Ibn Badis, BP 188 Mostaganem, Algeria)

  • Safia Nait Bahloul

    (Department of Computer Science, Université d'Oran Es-Sénia, BP 1524 El M'naouer, Oran, Algeria)

Abstract

This paper deals with the problem of multicriteria clusters construction. The aim is to propose a multicriteria clustering procedure aiming at discovering data structures from a multicriteria perspective by defining a dissimilarity measure which takes into account the multicriteria nature of the problem. Comparing two objects in the multicriteria context is based on the preference information that expresses whether these objects are indifferent, incomparable or one is preferred to the other. The proposed approach uses this preference information with an agreement–disagreement similarity index to compute a dissimilarity measure. The approach generates, according to the preference relations, a set of clusterings. Each clustering expresses a way of grouping objects according to the preference relation used. A good quality final clustering is obtained by combining the clusterings generated previously using a clustering ensemble technique.

Suggested Citation

  • Baroudi Rouba & Safia Nait Bahloul, 2014. "A Multicriteria Clustering Approach Based on Similarity Indices and Clustering Ensemble Techniques," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 811-837.
    • Handle: RePEc:wsi:ijitdm:v:13:y:2014:i:04:n:s0219622014500631
    DOI: 10.1142/S0219622014500631
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    References listed on IDEAS

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    1. Stefan Eppe & Julien Roland & Yves De Smet, 2014. "On the use of valued action profiles for relational multi-criteria clustering," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 4(3), pages 201-233.
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    Cited by:

    1. Díaz, Raymundo & Fernández, Eduardo & Figueira, José-Rui & Navarro, Jorge & Solares, Efrain, 2023. "A new hierarchical multiple criteria ordered clustering approach as a complementary tool for sorting and ranking problems," Omega, Elsevier, vol. 117(C).
    2. Ishizaka, Alessio & Lokman, Banu & Tasiou, Menelaos, 2021. "A Stochastic Multi-criteria divisive hierarchical clustering algorithm," Omega, Elsevier, vol. 103(C).
    3. Małgorzata Przybyła-Kasperek, 2019. "Three Conflict Methods in Multiple Classifiers that Use Dispersed Knowledge," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 555-599, March.
    4. Giyasettin Ozcan, 2018. "Unsupervised Learning from Multi-Dimensional Data: A Fast Clustering Algorithm Utilizing Canopies and Statistical Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 841-856, May.
    5. Juan Carlos Leyva Lopez & Jesus Jaime Solano Noriega & Diego Alonso Gastelum Chavira, 2017. "A Multi-Criteria Approach to Rank the Municipalities of the States of Mexico by its Marginalization Level: The Case of Jalisco," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 473-513, March.
    6. Shen-Tsu Wang, 2018. "An Analysis of the Optimal Customer Clusters Using Dynamic Multi-Objective Decision," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 547-582, March.

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