Abstract
The density based algorithms such as DBSCAN is considered as one of the most common and powerful algorithms in data clustering with the noise datasets. DBSCAN based algorithm’s is able to find out clusters with the different shape and variable size. However it is failed to detect the correct clusters, if there is density variation within the clusters. This paper presents new way to solve the problem of detecting the clusters of varying density which most of the DBSCAN based algorithms can’t deal with it correctly. Our proposed approach is depending on oscillation of clusters which is obtained by applying basic DBSCAN algorithm to conflation it in a new clusters, the proposed algorithm help to decide whether the different density regions belong to the same cluster or not. The experimental results showed that the proposed clustering algorithm gives satisfied results on different Data sets.
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Arya, R., Sikka, G. (2014). An Optimized Approach for Density Based Spatial Clustering Application with Noise. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Advances in Intelligent Systems and Computing, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-03107-1_76
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DOI: https://doi.org/10.1007/978-3-319-03107-1_76
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03106-4
Online ISBN: 978-3-319-03107-1
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