An empirical study on multi-objective genetic algorithms using clustering techniques
Mrs. P. Anusha, J.G.R. Sathiaseelan · International Journal of Advanced Intelligence Paradigms · 2016
Clustering is a data mining technique widely used to find similar group of data. A better cluster always have most similar data while the elements from the different clusters are dissimilar. Genetic algorithms (GAs) are considered as a global searching technique for optimisation problems. In the recent years there are many conflicting measure of objectives which are need to be optimised concurrently to achieve a tradeoff. Traditionally, evolutionary algorithms (EAs) were used to solve single objective problems. Optimum performance in single objective optimisation often results low, when the situation deals with more than one objective. This situation creates a bottleneck for an alternate technique called as multi-objective optimisation using genetic algorithms which aids to find more solutions in data mining domain.