Moderm Techniques used for Big Data Clustering:A Review
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 6
Abstract
Huge quantity of data is generated in each second in the modern digital era. It became almost impossible for the conventional data analytics frameworks to handle this ever-exploding big data. Storage spaces for this huge amount of data is also running out. Big data clustering algorithms help up to huge extent where dimensionality of enormous big data is reduced to a great extent without any significant loss of relevant information. Big data clustering algorithms need not only be robust in working but also need to be compliant for distributed and parallel processing. This paper is reviewing the most modern and robust big data clustering algorithms. Four algorithms are chosen, one from each category of Partitioning based, Density based, Grid based, Model-based big data clustering algorithms. Algorithms considered for in depth review are partition based big data clustering in a distributed Apache spark framework, Density Based Spatial Clustering of Applications with Noise, grid based OptiGrid clustering algorithm and model based Self-organizing Map clustering algorithm. Detailed algorithm working and shortcomings of each of these algorithms are discussed in detail.
Authors and Affiliations
Sharon Susan Jacob, Prof. R. Vijaya kumar
Keywords
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