Clustering is a procedure of grouping a collection of certain objects into a relevant sub-group. Each sub-group is called as a cluster, which guides users to comprehend the collections in a data set. It is an unsupervised learning technique where each dispute of this type deals with discovering a structure during the accumulation of unlabeled data. Statistics, Pattern Recognition, Machine learning are some of the active research in the theme of Clustering techniques. A Large and Multivariate database is built upon excellent data mining tools in the analysis of clustering. Many types of clustering techniques are— Hierarchical, Partitioning, Density–based, Model based, Grid–based, and Soft-Computing techniques. In this paper a comparative study is done on Agglomerative Hierarchical, K-Means, Affinity Propagation and DBSCAN Clustering and its Techniques.
CITATION STYLE
Kumar*, J. K., & Seshashayee, Dr. M. (2020). A Relative Examination on Clustering Techniques: Agglomerative, K-Means, Affinity Propagation and DBSCAN. International Journal of Innovative Technology and Exploring Engineering, 9(3), 2018–2020. https://doi.org/10.35940/ijitee.c8668.019320
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