An Optimized Approach for Density Based Spatial Clustering Application with Noise

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

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.

Keywords

Core point DBSCAN Eps RegionQuery MinPts 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electronics & Information TechnologyNational Informatics Center (NIC)New DelhiIndia
  2. 2.Department of Computer ScienceNational Institute of TechnologyJalandharIndia

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