A Parameter free Clustering of Density Based Algortihm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2018, Vol 20, Issue 5
Abstract
Clustering is a kind of unsupervised learning process in data mining and pattern recognition, most of the clustering algorithms are sensitive to their input parameters. So it is necessary to evaluate results of the clustering algorithms. It is difficult to define which clustering designs are acceptable hence several clustering validation measures are developed. In the present paper we have a study of implementation on some of very common data sets with internal index compared to our new and improved parameter free Density Based Clustering with the help of differential evolution. Density based clustering methods are being used for clustering spatial databases with noise. Density Clustering of Spatial Data and its Application with noise (DBSCAN) can discover clusters of arbitrary shapes and sizes effectively with the help of Eps(radius of the cluster) and MinPts (minimum number of points to be inside the cluster). The value of these parameters is very important in determining clustering results as the output varies significantly with the little changes in the values and is also very hard to determine these parameters a priori. In this paper we present a new algorithm named Parameter free Density Based Algorithm using Differential evolutions, which uses the combination of analytical ways to determine the efficient values of Eps and Minpts using Differential Evolution Method. The Experimental results show that our algorithm is precise in selecting the parameters and efficient.
Authors and Affiliations
Mr. Aakash Kulmitra, Mr. Ram Nivas Giri
Keywords
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- EP ID EP402130
- DOI 10.9790/0661-2005030916.
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