A Relative Examination on Clustering Techniques: Agglomerative, K-Means, Affinity Propagation and DBSCAN
J.Kiran Kumar1, M.Seshashayee2

1J. Kiran Kumar*, PG Student, Department of CS,GIS,GITAM(Deemed to be University), Visakhapatnam, India.
2Dr. M. Seshashayee, Department of CS,GIS,GITAM (Deemed to be University), Visakhapatnam, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 2018-2020 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8668019320/2020©BEIESP | DOI: 10.35940/ijitee.C8668.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Agglomerative Hierarchical Clustering, K-Means Clustering, Affinity Propagation, DBSCAN
Scope of the Article: Clustering