Recent clustering paper

 Zhou, Bing, Lu, Bei and Saeidlou, Salman 2022. A hybrid clustering method based on the several diverse basic clustering and meta-clustering aggregation technique. Cybernetics and Systems. 53 (7), pp. 1-27. https://doi.org/10.1080/01969722.2022.2110682

A hybrid clustering method based on the several diverse basic clustering and meta-clustering aggregation technique

Abstract

In hybrid clustering, several basic clustering is first generated and then for the clustering aggregation, a function is used in order to create a final clustering that is similar to all the basic clustering as much as possible. The input of this function is all basic clustering and its output is a clustering called clustering agreement. However, this claim is correct if some conditions are met. This study has provided a hybrid clustering method. This study has used the basic k-means clustering method as a basic cluster. Also, this study has increased the diversity of consensus by adopting some measures. Here, the aggregation process of the basic clusters is done by the meta-clustering technique, where the primary clusters are re-clustered to form the final clusters. The proposed hybrid clustering method has the advantages of k-means, its high speed, as well as it does not have its major weaknesses, the inability to detect non-spherical and non-uniform clusters. In the empirical studies, we have evaluated the proposed hybrid clustering method with other up-to-date and robust clustering methods on the different datasets and compared them. According to the simulation results, the proposed hybrid clustering method is stronger than other clustering methods.

Keywords Artificial intelligence; Information systems; Software; Aggregation techniques; Diversity of clustering; Hybrid clustering; Meta-clustering

Year 2022

Journal Cybernetics and Systems

Journal citation 53 (7), pp. 1-27

Publisher Taylor & Francis

ISSN 0196-9722

1087-6553

Digital Object Identifier (DOI) https://doi.org/10.1080/01969722.2022.2110682

Official URL https://www.tandfonline.com/doi/full/10.1080/01969722.2022.2110682

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