Rouhollah Maghsoudi - Department of Computer, Nour Branch, Islamic Azad University, Nour, Iran Arash Ghorbannia Delavar - Payame Noor University, Tehran, Iran Somayye Hoseyny - Payame Noor University of Shahrerey Rahmatollah Asgari - Islamic Azad University of Semnan Yaghub Heidari - Department of Electrical, Nour Branch, Islamic Azad University, Nour, Iran
Data clustering into appropriate classes and categories is one of the important topic in pattern recognition. It is very good and very efficient that the number of data which misclassified is minimized or in other words data that classified in each class has been possible as much possible similarity together. In this article at the first, a fundamental method of data clustering which named K-Means Clustering was expressed and then with genetic algorithm , our proposal model that we named it GA-Clustering for improving K-Means method has been introduced. Finally, the said model was examined on some of the well-known data set. Results show that our method clusters data better than traditional K-Means Clustering algorithm significantly.
Rouhollah Maghsoudi, Arash Ghorbannia Delavar, Somayye Hoseyny, Rahmatollah Asgari, Yaghub Heidari, Representing the New Model for Improving K-means Clustering Algorithm Based on Genetic Algorithm, Journal of Mathematics and Computer Science, 2 (2011), no. 2, 329--336
Maghsoudi Rouhollah, Ghorbannia Delavar Arash, Hoseyny Somayye, Asgari Rahmatollah, Heidari Yaghub, Representing the New Model for Improving K-means Clustering Algorithm Based on Genetic Algorithm. J Math Comput SCI-JM. (2011); 2(2):329--336
Maghsoudi, Rouhollah, Ghorbannia Delavar, Arash, Hoseyny, Somayye, Asgari, Rahmatollah, Heidari, Yaghub. "Representing the New Model for Improving K-means Clustering Algorithm Based on Genetic Algorithm." Journal of Mathematics and Computer Science, 2, no. 2 (2011): 329--336
