A Fitness-Based Adaptive Differential Evolution Approach to Data Clustering

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means (FCM) is one of the most popular clustering methods based on minimization of a criterion function as it works fast in most scenarios. However, it is sensitive to initialization and is easily trapped in local optima. In this work, a fuzzy clustering (FC) algorithm based on Differential Evolution (DE) is proposed. Here we use a DE with Fitness Based Adaptive Technique (FBADE) for the adaptation of DE parameters. 3 well-known data sets viz. Iris, Wine, Motorcycle and 2 synthetic datasets are used to demonstrate the effectiveness of the algorithm. The resulting algorithm is compared with conventional Fuzzy C-Means (FCM) algorithm, FCM with DE (FCM-DE), FCM with Self Adaptive DE (FCM-SADE).


Differential Evolution Fuzzy Clustering Global Optimization Evolutionary Algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • G. R. Patra
    • 1
  • T. Singha
    • 1
  • S. S. Choudhury
    • 1
  • S. Das
    • 2
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Engineering and Communication Sciences UnitIndian Statistical InstituteKolkataIndia

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