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Clustering Techniques for Big Data Analysis
Abstract
Clustering is the process by which data is classified into semantically consistent clusters based on some measure of similarity. Typically, clustering is an unsupervised machine learning problem, meaning that the structure of the data must be detected without any label being available as to which category it belongs to. Various clustering techniques have been developed, which aim to find coherent groups among a large number of data registered in large databases. We could say that the clustering technique is directly related to the optimization technique and thus its applications multiply in finding homogeneous groups of elements. This work deals with clustering algorithms and their application to big data. First, the clustering concept, objectives, and techniques are studied. Then, the main clustering algorithms are analyzed, their positive and negative characteristics, the steps to be followed for their application, their mathematical formulas, and a small application for each one on a small data set.
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