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T. Abdul Razak
Jamal Mohamed College
Computer Science & IT

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Density Based Clustering with Integrated One-Class SVM for Noise Reduction K. Nafees Ahmed; T. Abdul Razak
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 6, No 3: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (503.376 KB) | DOI: 10.11591/ijict.v6i3.pp199-208

Abstract

Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.
Co-Authors K. Nafees Ahmed