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Improving Density-based Clustering using Metric Optimization

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Wesam M. Ashour, Islam A. Mezied, Abdallatif S. Abu-Issa
10.5120/ijca2018917932

Wesam M Ashour, Islam A Mezied and Abdallatif S Abu-Issa. Improving Density-based Clustering using Metric Optimization. International Journal of Computer Applications 181(21):36-43, October 2018. BibTeX

@article{10.5120/ijca2018917932,
	author = {Wesam M. Ashour and Islam A. Mezied and Abdallatif S. Abu-Issa},
	title = {Improving Density-based Clustering using Metric Optimization},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2018},
	volume = {181},
	number = {21},
	month = {Oct},
	year = {2018},
	issn = {0975-8887},
	pages = {36-43},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume181/number21/30013-2018917932},
	doi = {10.5120/ijca2018917932},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Density-based clustering is one of the most important sciences nowadays. A various number of datasets depend on it. Since homogeneous clustering may generate a large number of smaller useless clusters, a good clustering method should give the permission to a significant density variation. This paper focuses on enhancing the clustering results after using density-based cluster algorithms DBSCAN (Density-based spatial clustering of applications with noise) or OPTICS (Ordering points to identify the clustering structure) by using statistical models. The use of statistical models supports improving results by reducing the number of noise points with the same cluster number and expand the selected area as recognized as cluster.

References

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Keywords

Density-based, DBSCAN, OPTICS, Statistical, Selection model