Heuristic for the data clustering problem

A method, in a computer system comprising a display, one or more processors and a memory storing a program, the program when executed performs the method comprising:

• inputting, to the computer system, a color image having a plurality of pixels and a number of clusters;
• distances between centroids of the clusters using greedy search, including selecting new centroids that have maximum distance from the centroids of others of the clusters and that improve a value of a cluster validity index;
• performing an evolutionary-type search to adjust the new centroids of the clusters with respect to the validity index that considers compactness of the clusters as well as separation between clusters;
• using the adjusted centroids, assign each pixel to the cluster whose centroid has the minimum Euclidean distance between the pixel and the centroid of the cluster;
• storing, in a storage device, a compressed image in which each pixel is stored with the assigned cluster, and the centroids of all clusters;
• and
• displaying, by the display, a compressed image in which each pixel is displayed with the centroid of the assigned cluster,
• wherein the centroids include attributes representing genes and the evolutionary-type search adjusts the centroids by
• selecting a subset of the genes;
• performing a mutation operation on a selected subset of genes to obtain a mutant solution;
• determining the validity index based on a ratio of separation between clusters to the compactness of the clusters for the clusters before the mutation operation and after the mutation operation and
• replacing a value of an attribute of a solution with the value of the mutant solution in response to a determination that the determined validity index after the mutation operation is greater than or equal to the validity index before the mutation operation.