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Neural Computing and Applications

Erschienen in:

19.08.2016 | Original Article

Memory-enriched big bang–big crunch optimization algorithm for data clustering

verfasst von: Kayvan Bijari, Hadi Zare, Hadi Veisi, Hossein Bobarshad

Erschienen in: Neural Computing and Applications | Ausgabe 6/2018

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Abstract

Cluster analysis plays an important role in decision-making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang–big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory-based scheme as compared to its similar heuristic techniques. Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets. The experimental results show the significant superiority of the proposed method over the similar algorithms.

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Titel: Memory-enriched big bang–big crunch optimization algorithm for data clustering
verfasst von: Kayvan Bijari
Hadi Zare
Hadi Veisi
Hossein Bobarshad
Publikationsdatum: 19.08.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2528-9

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