Publication Date: 2023/05/04
Abstract: - A vital data mining method for analysing large records is clustering. Utilising clustering techniques for enormous data presents hurdles in addition to potential new issues brought on by massive datasets. The question is how to deal with this hassle and how to install clustering techniques to big data and get the results in a reasonable amount of time given that large information is related to terabytes and petabytes of information and clustering algorithms are come with excessive computational costs. This paper aims to evaluate the design and development of agglomeration algorithms to address vast knowledge difficulties, starting with initially proposed algorithms and ending with contemporary unique solutions The techniques and the key challenges for developing advanced clustering algorithms are introduced and examined, and afterwards the potential future route for more advanced algorithms is based on computational complexity. In this study, we address big data applications for actual world objects and clustering techniques.
Keywords: Big Data, Clustering Algorithms, Computational complexity, Partition based Algorithms, Hierarchical Algorithms.
DOI: https://doi.org/10.5281/zenodo.7894496
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23APR1454.pdf
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