In an aeon where information is precious, all these data need to uncover the relations presented between a set of unlabeled dataset for the purposes of manifold - revise, explore, sort, store, analyze; arises all the more. A very feasible way to explore the relations between data is clustering, an unsupervised data mining technique. Clustering aims to group like data points together in clusters with no similarity between data points of different clusters and leaves behind outliers or points not belonging to any of the clusters. Clustering can be applied to all types of data with varying nature (numeric, categorical, mixed), and dimensions (low, high), however, methodologies and similarity measures that can be applied may vary accordingly. In this manuscript we will discuss about various technologies used for clustering of data like role of distance metrics in clustering, clustering using ensembles and dimensionality reduction/minimaization techniques for modeling complex data relations.

Clustering,Distance Metric Styling, Ensembling ,Large Dimensions

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