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Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset

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Abstract

Many conventional optimization approaches concentrate more on addressing only one appropriate solution. Thus, these methods were to be utilized often, hence there were no chances of producing the intended solution. Therefore, the issue of multimodal optimization has to be considered. So, to reduce the difficulties by the clustering and further, it followed by the optimization technique. Here, the variety of real-time and artificial techniques are used. Using the FCDP-Fast Clustering with Density Peak, we calculate the density values after determining the center with the help of objective function. Then, the fuzzy clustering is applied to form the clustered groups with the density and center values. Finally, we optimize the data using the CDE-Crowding Differential Evaluation methodology. Performance analysis is then proceeded with some existing methods by using the performance metrics like NMI and ARI. After validation, it concluded that the proposed method was superior to the existing method.

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Authors and Affiliations

  1. Cse Department, Sreyas Institute of Engineering and Technology, Hyderabad, India

    G. Surya Narayana

  2. Department of CSE, Geethanjali College of Engineering and Technology, Cheeryal Village, Hyderabad, India

    Kamakshaiah Kolli

Authors
  1. G. Surya Narayana
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  2. Kamakshaiah Kolli
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Correspondence to G. Surya Narayana.

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Narayana, G.S., Kolli, K. Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset. Multimed Tools Appl 80, 4769–4787 (2021). https://doi.org/10.1007/s11042-020-09718-4

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  • DOI: https://doi.org/10.1007/s11042-020-09718-4

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