IOG - An Improved Approach to Find Optimal Grid Size Using Grid Clustering Algorithm

Abstract

The grid-clustering algorithm is the most important type in the hierarchical clustering algorithm. The grid-based clustering approach considers cells rather than data points. In grid-based clustering, all the clustering operations are performed on the segmented data space, rather than the original data objects. Grid- based methods are highly popular compared to the other conventional models due to their computational efficiency but to find optimal grid size is a key feature in grid-based clustering algorithm. There exist some algorithm in that they achieve optimal grid size but in real life data can be dense or sparse. So, in these research to develop an algorithm that can find optimal grid size in any type of dataset in dense or sparse with appropriate accuracy or maintaining the accuracy with less time.