A novel clustering approach: Artificial Bee Colony (ABC) algorithm

KARABOĞA D., ÖZTÜRK C.

APPLIED SOFT COMPUTING, cilt.11, sa.1, ss.652-657, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 1
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.asoc.2009.12.025
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.652-657
  • Anahtar Kelimeler: Classification, Clustering analysis, Artificial Bee Colony algorithm, Particle Swarm Optimization, PARTICLE SWARM OPTIMIZATION, NEURAL-NETWORKS, MULTIVARIATE DATA, CLASSIFICATION
  • Erciyes Üniversitesi Adresli: Evet

Özet

Artificial Bee Colony (ABC) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm. Clustering analysis, used in many disciplines and applications, is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, ABC is used for data clustering on benchmark problems and the performance of ABC algorithm is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering. (C) 2009 Elsevier B.V. All rights reserved.