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Computational Intelligence in Data Mining pp 351–365Cite as

A Comprehensive Analysis of Kernelized Hybrid Clustering Algorithms with Firefly and Fuzzy Firefly Algorithms

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 990))

Abstract

In order to handle the problem of linear separability in the early data clustering algorithms, Euclidean distance is being replaced with Kernel functions as measures of similarity. Another problem with the clustering algorithms is the selection of initial centroids randomly, which affects not only the final result but also decreases the convergence rate. Optimal selection of initial centroids through optimization algorithms like Firefly or Fuzzy Firefly algorithms provide partial solution to this problem. In this paper, we focus on two kernels; Gaussian and Hyper-tangent and use both Firefly and Fuzzy Firefly algorithms separately along with algorithms like FCM, IFCM and RFCM and analyse their efficiency using two measures DB and D. Our analysis concludes that RFCM with Hyper-tangent kernel and fuzzy firefly produce the best results with fastest convergence rate. We use the two images; MRI scan of a human brain and blood cancer cells for our analysis.

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Abbreviations

FCM:

Fuzzy C-Means

RFCM:

Rough Fuzzy C-Means

IFCM:

Intuitionistic Fuzzy C-Means

FCMFA:

Fuzzy C-Means with Firefly algorithm

FCMFFA:

Fuzzy C-Means with Fuzzy Firefly algorithm

GKFCM:

Gaussian Kernelized Fuzzy C-Means

HKFCM:

Hyper-tangent Fuzzy C-Means

IFCMFA:

Intuitionistic Fuzzy C-Means with Firefly algorithm

IFCMFFA:

Intuitionistic Fuzzy C-Means with Fuzzy Firefly algorithm

RFCMFA:

Rough Fuzzy C-Means with Firefly algorithm

RFCMFFA:

Rough Fuzzy C-Means with Fuzzy Firefly algorithm

References

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, 632014, Tamil Nadu, India

    B. K. Tripathy & Anmol Agrawal

Authors
  1. B. K. Tripathy
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  2. Anmol Agrawal
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Corresponding author

Correspondence to Anmol Agrawal .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Tripathy, B.K., Agrawal, A. (2020). A Comprehensive Analysis of Kernelized Hybrid Clustering Algorithms with Firefly and Fuzzy Firefly Algorithms. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_31

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