Abstract: Data mining is the method of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Clustering performs an important role in the reference composition of data analysis. Clustering is a famous data analysis and data mining problem. Symmetry can be studied as a pre-attentive feature, which can enhance shapes and objects, as reconstruction and recognition. Clustering, recognized as a crucial issue of unsupervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for more understanding. This paper explains the different types of clustering and methods in data mining.
Keywords: Data Mining, Clustering, Algorithm, k-means Clustering.
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DOI:
10.17148/IJARCCE.2022.111211
