Survey Paper on Quality Cluster Generation Using Random Projections
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Abstract
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 will obtained by using densitybased clustering and DBSCAN clustering. DBSCAN cluster is a fast clustering technique, large complexity and requires more parameters. To overcome these problems uses the OPTICS Density-based algorithm. The algorithm requires single factor, namely the least amount of points in a cluster which can necessary as input in density- based technique. Using random projection improving the cluster quality and runtime.
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