TY  - JOUR
T1  - Improved Fuzzy Ant-Based Clustering: A Nonparametric Balance Between Exploitation and Exploration
AU - Supratid, Siriporn AU - Julrode, Phichete 
JO  - Research Journal of Applied Sciences
VL  - 8
IS  - 9
SP  - 425
EP  - 434
PY  - 2013
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2013.425.434
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2013.425.434
KW  - Fuzzy c-means
KW  -ant-based clustering
KW  -nonparametric
KW  -exploration
KW  -exploitation
KW  -divide and conquer
AB  - Fuzzy ant-based clustering algorithm has been efficiently 
  employed to serve real-world applications. Although, ant-based clustering algorithm 
  can relieve the fast convergence during the search, limitation of such an algorithm 
  to overcome the problems of local optimal traps along with divergence of the 
  search are still non-trivial. Striking the balance between exploitation and 
  exploration of the search is one of the significant keys to overcome such problems 
  thus leads to achieve the global optimal solution. Nevertheless, arbitrarily 
  defined parameters are usually used to control the cycle of exploitation and 
  exploration mechanisms thus may lead to a biased and overly optimistic learning 
  process. This study proposes an improved version of the fuzzy ant-based clustering. 
  The objective is to apply a nonparametric method of balancing exploitation and 
  exploration search during ant-based clustering, aiming to accomplish the global 
  optimal solution. The criteria of performance evaluation rely on F-measures, 
  FCM objective degree, Xie-Beni validity index and runtime as well. The experimental 
  results, based on both real-world and artificial data sets indicate the high 
  performance of the proposed method over the comparatively effective clustering 
  algorithms.
ER  -