Enhancing of DBSCAN based on Sampling and Densitybased Separation

Authors

  • Safaa Al-mamory University of Information Technology and Communications (UOITC)
  • Israa Kamil University of Babylon

DOI:

https://doi.org/10.25195/ijci.v42i1.82

Keywords:

Density-based, DBSCAN, different densities, Sampling.

Abstract

DBSCAN (Density-Based Clustering of Applications with Noise )is one of the attractive algorithms among densitybased clustering algorithms. It characterized by its ability to detect clusters of various sizes and shapes with the presence of noise, but its performance degrades when data have different densities .In this paper, we proposed a new technique to separate data based on its density with a new sampling
technique , the purpose of these new techniques is for getting data with homogenous density .The experimental results on
synthetic data and real world data show that the new technique enhanced the clustering of DBSCAN to large extent.

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Author Biography

Safaa Al-mamory, University of Information Technology and Communications (UOITC)

College of Business Informatics

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Published

2016-12-31

Issue

Vol. 42 No. 1 (2016): Iraqi Journal for Computers and Informatics

Section

Articles

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