A genetic clustering algorithm guided by a descent algorithm
G.P. Scott, D.I. Clark, Tung The Pham · 2002
This paper considers a clustering problem where distorted images are allocated into a specific number of clusters such that each cluster is composed of images that are similar. Similarity is determined by summing the mean squared error of each image of a cluster with the centroid of that cluster. A genetic algorithm guided by a descent algorithm is presented to minimise this error. Tabu search is also employed to maintain genetic diversity and improve efficiency. Experiments use monochrome, greyscale and colour images.