Improving fuzzy c-means clustering via quantum-enhanced weighted superposition attraction algorithm

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BAYKASOĞLU A., Golcuk I., ÖZSOYDAN F. B.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.48, sa.3, ss.859-882, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48 Sayı: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.15672/hjms.2019.655
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.859-882
  • Anahtar Kelimeler: Fuzzy c-means clustering, Metaheuristics, Pattern recognition, Weighted superposition attraction, SWARM INTELLIGENCE ALGORITHM, DYNAMIC OPTIMIZATION, COLONY APPROACH, EVOLUTIONARY, WSA
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Fuzzy clustering has become an important research field in pattern recognition and data analysis. As supporting unsupervised mode of learning, fuzzy clustering brings about unique opportunities to reveal structural relationships in data. Fuzzy c-means clustering is one of the widely preferred clustering algorithms in the literature. However, fuzzy c-means clustering algorithm has a major drawback that it can get trapped at some local optima. In order to overcome this shortcoming, this study employs a new generation metaheuristic algorithm. Weighted Superposition Attraction Algorithm (WSA) is a novel swarm intelligence-based method that draws inspiration from the superposition principle of physics in combination with the attracted movement of agents. Due to its high converging capability and practicality, WSA algorithm has been employed in order to enhance performance of fuzzy-c means clustering. Comprehensive experimental study has been conducted on publicly available datasets obtained from UCI machine learning repository. The results point out significant improvements over the traditional fuzzy c-means algorithm.