GAKREM: A novel hybrid clustering algorithm

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

“…(14); else go to step (3). (14) Find all chromosomes whose cluster numbers are equal to from the population in step (11). The new population is composed of these chromosomes.…”

“…To overcome the first shortcoming, some global optimization techniques have been introduced to deal with data clustering problems in the past years, for example, simulated annealing-(SA-) based [5], particle swarm optimization-(PSO-) based [6][7][8], genetic algorithms-(GA-) based [9][10][11], and quantum genetic algorithms-(QGA-) based techniques [12]. In recent years, genetic algorithms have been used to automatically determine the number of clusters by using variable-length strings [13,14].…”

KMGEM: Data Clustering by Combination of K-Means and Grenade Explosion Algorithm

“…The main purpose of utilizing clustering is to find common classification of objects. Clustering analysis procedures can be applied to a variety of problems, as an example it can be used in subjective interpretation and data compression, local model advancement, process monitoring, analysis of substance mixes for combinatorial science, finding of clusters in DNA dinucleotide, classification of coals, assembling and generation (process optimization and investigating), money related venture, pharmaceutical (a few indicative data put away by clinic administration frameworks), atomic science, radar checking and innovative work arranging, and telecom system [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In addition to these usages, one of the main advantages of clustering techniques is that they can be used for solving problems with very large data.…”

“…In this paper, we proposed a partitioned clustering technique that is based on a well-known partitioned clustering strategy called K-means clustering. The K-mean clustering algorithm is a standout amongst the most productive clustering algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

“…The results we obtain from K-means algorithm intensively rely on the starting decision of the cluster centers. To conquer this disadvantage, numerous clustering algorithms in light of evolutionary algorithms, for example, genetic algorithm (GA) and simulation annealing (SA) have been presented [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. For example, in [1] they have suggested a crossover procedure in view of K-means algorithm, Nelder-Mead simplex search, and Particle Swarm Optimization (PSO) for cluster analysis.…”