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Volume 6 | Issue 2

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Paper Title: Comparative Study on Various Density Based Clustering and its types.
Authors Name: Aakash Kulmitra , Mr. Ram Nivas Giri
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Published Paper Id: IJNRD1706025
Published In: Volume 2 Issue 6, June-2017
Abstract: Clustering is a kind of unsupervised learning process in data mining and pattern recognition and most of the clustering algorithms are sensitive to their input parameters. So it is necessary to evaluate results of the clustering algorithms. But it is difficult to define which clustering designs are acceptable hence several clustering validation measures are developed. Clustering is used in various fields such as pattern recognition. In the following paper we will be studying about various density based scanning algorithms and there implementation on some of very common data sets with various output. Density based clustering methods are used for clustering spatial databases with noise. Density based Spatial Clustering of Applications with noise (DBSCAN) can discover clusters of arbitrary shapes and sizes effectively we will be seeing this in the following paper. We will be comparing DBSCAN algorithm with various density based scanning algorithms and will be calculating and the results with the datasets with noise.
Keywords: DBSCAN, Clustering algorithm, Spatial Clustering, Unsupervised learning.
Cite Article: "Comparative Study on Various Density Based Clustering and its types. ", International Journal of Novel Research and Development (, ISSN:2456-4184, Vol.2, Issue 6, page no.143-147, June-2017, Available :
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