Analysis of Extended Performance for clustering of Satellite Images Using Bigdata Platform Spark

Due to the recent emergence Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, targeted marketing, digital forensics, etc. As the satellite imagery is getting generated at a higher rate than the previous decades, it becomes essential to have better solutions in terms of accuracy as well as performance. In this paper, we are proposing the solution over big data which performs the clustering of images using different methods viz. Scalable Kmeans++, Bisecting Kmeans and Gaussian Mixture. Since the number of clusters is not known in advance in any of the methods, we also propose a better approach of validating the number of clusters using Simple Silhouette Index algorithm and thus to provide the better clustering possible. Keyword: Images, Distributed Processing, Scalable Kmeans++, K-means Clustering, Bigdata, Datamining, Security, Gaussian Mixture