An empirical study on multi-objective genetic algorithms using clustering techniques
by M. Anusha; J.G.R. Sathiaseelan
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 8, No. 3, 2016

Abstract: 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.

Online publication date: Mon, 04-Jul-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com