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Data Clustering with Differential Evolution Incorporating Macromutations

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8297))

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Abstract

Data clustering is one of the fundamental tools in data mining and requires the grouping of a dataset into a specified number of nonempty and disjoint subsets. Beside the usual partitional and hierarchical methods, evolutionary algorithms are employed for clustering as well. They are able to find good quality partitions of the dataset and successfully solve some of the shortcomings that the k-means, being one of the most popular partitional algorithms, exhibits. This paper proposes a differential evolution algorithm that includes macromutations as an additional exploration mechanism. The application probability and the intensity of the macromutations are dynamically adjusted during runtime. The proposed algorithm was compared to four variants of differential evolution and one particle swarm optimization algorithm. The experimental analysis conducted on a number of real datasets showed that the proposed algorithm is stable and manages to find high quality solutions.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, J. J. Strossmayer University of Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia

    Goran Martinović & Dražen Bajer

Authors
  1. Goran Martinović
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  2. Dražen Bajer
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Editor information

Editors and Affiliations

  1. Electrical Engineering Dept., IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, Kancheepuram, 603203, Tamil Nadu, India

    Shubhransu Sekhar Dash

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Martinović, G., Bajer, D. (2013). Data Clustering with Differential Evolution Incorporating Macromutations. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-03753-0_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03752-3

  • Online ISBN: 978-3-319-03753-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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