Clustering of Image Data Set Using K-Means and Fuzzy K-Means Algorithms

Clustering or data grouping is a key initial procedure in image processing. In present scenario the size of database of companies has increased dramatically, these databases contain large amount of text, image. They need to mine these huge databases and make accurate decisions in short durations in order to gain marketing advantage. As image is a collection of number of pixels. It is difficult to take account of all pixels for clustering. So the concept of image segmentation play very useful role in clustering as it save times and it is efficient too. With the use of k-mean and it's variant fuzzy k-means algorithm clustering of these large data become easy and time saving. This paper deals with the application of standard k-means and fuzzy k-means clustering algorithms in the area of image segmentation. In order to assess and compare both versions of k-means algorithm and fuzzy k-means, appropriate procedures implemented. Experimental results point that fuzzy logically optimized k-means algorithms proved their usefulness in the area of image analysis, yielding comparable and even better segmentation results.