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doi: 10.17586/2226-1494-2023-23-5-967-979

Clustering in big data analytics: a systematic review and comparative analysis (review article) 

H. Shili


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Article in English

For citation:
Shili H. Clustering in big data analytics: a systematic review and comparative analysis (review article). Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 5, pp. 967–979. doi: 10.17586/2226-1494-2023-23-5-967-979


Abstract
In the modern world, the widespread use of information and communication technology has led to the accumulation of vast and diverse quantities of data, commonly known as Big Data. This necessitates the need for novel concepts and analytical techniques to help individuals extract meaningful insights from rapidly increasing volumes of digital data. Clustering is a fundamental approach used in data mining to retrieve valuable information. Although a wide range of clustering methods have been described and implemented in various fields, the sheer variety complicates the task of keeping up with the latest advancements in the field. This research aims to provide a comprehensive evaluation of the clustering algorithms developed for Big Data highlighting their various features. The study also conducts empirical evaluations on six large datasets, using several validity metrics and computing time to assess the performance of the clustering methods under consideration.

Keywords: big data, clustering, data mining, empirical evaluations, performance metrics

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