Quality Cluster Generation Using Random Projections - PA , Gat; Kadam, KS (International Journal of Computer Sciences and Engineering, 2019)
Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. Clusters are obtained by using density based clustering and DBSCAN clustering. DBSCAN cluster is a fast clustering technique, large complexity and requires large parameters. To overcome of these problems uses the OPTICS density based algorithm. The algorithm requires the simply a single parameter, namely the least amount of points in a cluster which is required as input in density based technique. Using random projection improving the cluster quality and run time.