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Hybrid Fuzzy C-Means Using Bat Optimization and Maxi-Min Distance Classifier

  • Rahul Kumar
  • Rajesh DwivediEmail author
  • Ebenezer Jangam
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)

Abstract

Fuzzy c-means (FCM) is a frequently used clustering method because of its efficiency, simplicity and easy implementation. Major drawbacks of FCM are sensitivity to initialization and local convergence problem. To overcome the drawbacks, the proposed method describes a hybrid FCM using Bat optimization and Maxi-min classifier. Maxi-min classifier is used to decide the count of clusters and then pass that count to randomized fuzzy c-means algorithm, which improves the performance. Bat optimization is a global optimization method used for solving many optimization problems due to its high convergence rate. Two popular datasets from kaggle are used to show the comparison between proposed technique and the fuzzy c means algorithm in terms of performance. Experiment results showing that the proposed technique is efficient and the results are encouraging.

Keywords

Fuzzy clustering Maxi-min Distance classifier Bat optimization Randomize fuzzy clustering Big data processing 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology (ISM)DhanbadIndia
  2. 2.Vignan Foundation for Science Technology and ResearchVadlamudiIndia

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