Novel Technique for Density Based Clustering Using Neural Networks

Authors(2) :-Asha Devi, Saurabh Sharma

In order to separate similar and dissimilar type of data into different clusters, the clustering mechanism is used. This helps in analyzing the data that is given as input in more efficient manner. The EPS is computed in case when DBSCAN algorithm is applied on the data that is to be processed. The Euclidean distance is computed from the central point which is mainly the EPS point calculated. This helps in further defining the similar and dissimilar type of data present. The accuracy of clustering is minimized in cases where there is a dynamic calculation of EPS and static computation of Euclidean distance. The back propagation algorithm is used in this paper that computes the Euclidean distance in a dynamic manner. The DBSCAN algorithm has less complexity as compared to the other algorithms. It can also identify any type of shaped cluster which is difficult with in other algorithms. The identification of specific types of clusters is difficult in cases where objects are circulated in heterogeneous manner. As per the various experiments conducted here, it is seen that the accuracy of the algorithm is enhanced and the execution time is minimized.

Authors and Affiliations

Asha Devi
Research Scholar, Department of Computer Science and Engineering, Sri Sai University, Palampur, India
Saurabh Sharma
Assistant Professor,Department of Computer Science and Engineering, Sri Sai University, Palampur, India

Clustering, DBSCAN, Back-Propagation, Accuracy, Execution Time

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Publication Details

Published in : Volume 3 | Issue 8 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 347-352
Manuscript Number : IJSRSET173854
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Asha Devi, Saurabh Sharma, " Novel Technique for Density Based Clustering Using Neural Networks, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 8, pp.347-352, November-December-2017.
Journal URL : http://ijsrset.com/IJSRSET173854

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