An Adaptive Masker for the Differential Evolution Algorithm
Nassim Ammour · IOSR Journal of Computer Engineering · 2012
The automatic clustering problem of a large and complex data set into different homogeneous clusters is a challenging task.The choose of a variable length clusters centers using a good method to generate the maskers is an important phase in the evolution of a global search heuristics algorithms.In this paper, a new technique of real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically detects the optimal number of clusters and performs the proper partitioning from a data set.The effectiveness of the proposed technique is demonstrated by comparing it with other popular clustering algorithms.In the comparative study, a remote sensing data described in terms of feature vectors and a remote sensing image are used for the automatic partitioning and classifying tasks.