A Review of Clustering Algorithms


Author(s): Jinan Redha Mutar

Clustering is an unsupervised artificial intelligence methodology that has emerged as a good learning tool for evaluating the massive amounts of datasets made available by today's applications. There is an affluence of information available in the field of clustering, and many endeavors have been made to identify and evaluate it for a spectrum of uses; however, the major disadvantages of someone using a classical classification algorithm for big data analysis are their elevated complicity, humongous volume, variety, and generation rate. As a result, typical clustering algorithms for processing such data are rapidly becoming obsolete. This presents researchers with exciting problems in inventing new scalable and cost-effective clustering algorithms capable of extracting relevant information from huge volumes of data collected in numerous aspects of life. In this review, we categorize the review of big data with techniques clustering by identifying the main research concerns. Then, for each subject, we therefore provide an up-to-date review of research papers.

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