Seed Point Selection Algorithm in Clustering of Image Data

Massive amount of data are being collected in almost all sectors of life due to recent technological advancements. Various data mining tools including clustering is often applied on huge data sets in order to extract hidden and previously unknown information which can be helpful in future decision-making processes. Clustering is an unsupervised technique of data points which is separated into homogeneous groups. Seed point is an important feature of a clustering technique, which is called the core of the cluster and the performance of seed-based clustering technique depends on the choice of initial cluster center. The initial seed point selection is a challenging job due to formation of better cluster partition with rapidly convergence criteria. In the present research we have proposed the seed point selection algorithm applied on image data by taking the RGB features of color image as well as 2D data based on the maximization of Shannon's entropy with distance restriction criteria. Our seed point selection algorithm converges in a minimum number of steps for the formation of better clusters. We have applied our algorithm in different image data as well as discrete data and the results appear to be satisfactory. Also we have compared the result with other seed selection methods applied through K-Means algorithm for the comparative study of number of iterations and CPU time with the other clustering technique.