Optimized Map Reduce Based Shuffling Mechanism for Density Clustering

Most international organizations produce more information in a week than many people could read in a lifetime. Everyday hundreds of megabytes of data are distributed around the world, but it is no longer possible to monitor this increasingly rapid development – the growth is nearly exponential. So the basic problems with the management of the data are its high Dimensionality. Dimensionality reduction can also be seen as the process of deriving a set of degrees of freedom which can be used to reproduce most of the unpredictability. This work is about Dimensionality Reduction, which in turn is about converting data of very high dimensionality into data of much lower dimensionality such that each of the lower dimensions conveys much more information.