Automatic fuzzy partitioning approach using Variable string length Artificial Bee Colony (VABC) algorithm
Swarm intelligence based automatic fuzzy clustering is recently an important and interesting unsupervised learning problem. In this article, an automatic fuzzy clustering technique is proposed based on a novel version of Artificial Bee Colony (ABC) algorithm. The idea of variable length genotypes is introduced to the ABC, and a novel version of ABC, called Variable string length Artificial Bee Colony (VABC) algorithm, is proposed. The VABC algorithm is derived from the ABC by redefining or modifying some operations in the ABC: the fixed length strings are represented by using variable length strings, the scheme for producing candidate solutions is modified, and some mutation operations are introduced. Use of VABC allows the encoding of variable number of clusters. This makes the VABC based Fuzzy C-Means clustering technique (VABC-FCM) not require a priori specification of the number of clusters. Moreover, the VABC-FCM has powerful global search ability under rational parameter setting. Some artificial data sets and real-life data sets are applied to validate the performance of VABC-FCM. The experimental results show that VABC-FCM can automatically evolve the optimal number of clusters and find proper fuzzy partitioning for these data sets when a rational validity index is adopted. Finally, the performance of VABC-FCM is compared with those of the Variable string length Genetic Algorithm based Fuzzy C-Means clustering (VGA-FCM), Particle Swarm Optimization algorithm based Fuzzy C-Means clustering (PSO-FCM), and Differential Evolutional algorithm based Fuzzy C-Means clustering (DE-FCM). The results show that the VABC-FCM outperforms VGA-FCM, PSO-FCM and DE-FCM in most of the cases. (c) 2012 Elsevier B.V. All rights reserved.
variable string length artificial bee colony artificial bee colony swarm intelligence fuzzy c-means genetic algorithm automatic clustering