Improved CLARA-High-Dimensional Data Clustering Using Improved Clustering Large Applications

Abstract

High-dimensional data clustering mechanisms are appearing, based on information clamorous and low-quality difficulties. Many of the current clustering algorithms become implicitly ineffective if the algorithms' essential similarity measure is calculated between data points in the high-dimensional space. To this end, various projected based clustering algorithms have been proposed. But, most of them faced problems when clusters cover in subspaces with very less dimensionality. To this end, the partition based Improved Clustering Large Applications (ICLARA) mechanism is employed. It is an expansion to approach to trade with data comprising many objects to reduce computing time and RAM storage problems. The proposed describes various representations and provides the most suitable clustering as the result to work with large datasets. The proposed approach is compared with previous hierarchal based CURE (Clustering Using REpresentatives), BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and Partitional Distance-Based Projected Clustering (PDBPC) approaches.Also, we calculated the accuracy of all clustering techniques about their parameters configuration. The experimental results show the proposed Improved CLARA algorithm provides better accuracy compared with previous methods.