Experimental Study of Data Clustering Using K- Means and Modified Algorithms
The k-Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in exploratory data analysis. This paper presents results of the experimental study of different approaches to k-Means clustering, thereby comparing results on different datasets using Original k-Means and other modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and execution time.