Call for Paper - July 2020 Edition
IJCA solicits original research papers for the July 2020 Edition. Last date of manuscript submission is June 22, 2020. Read More

Improving Density-based Clustering using Metric Optimization

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

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

	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 = {},
	doi = {10.5120/ijca2018917932},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


  1. Amin Karami , Ronnie Johansson “Choosing DBSCAN Parameters Automatically using Differential Evolution”, 2014.
  2. Krista Rizman, Zalik. An efficient “k-means clustering algorithm.Pattern Recognitoin Letters”, 2008.
  3. Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, 1996.
  4. Mohammed T. H. Elbatta, and Wesam M. Ashour, “A Dynamic Method for Discovering Density Varied Clusters”,2013
  5. Manisha Naik Gaonkar & Kedar Sawant ,“AutoEpsDBSCAN : DBSCAN with Eps Automatic for Large Dataset”, 2013.
  6. Anne Denton, “Density-based Clustering of Time Series Subsequences”, 2005
  7. A. Hinneburg and D. Keim. “A general approach to clustering in large databases with noise”, 2003.
  8. Ankerst, M., Breunig, M., Kriegel, H.P., Sander, J. “OPTICS: Ordering Points To Identify the Clustering Structure”, 1999.
  9. Vladimir Spitalsky, Marian Grendar “OPTICS-based clustering of emails represented by quantitative profiles”, 2013.
  10. Walter Zucchini, “An Introduction to Model Selection”, 2000
  11. Hamparsum Bozdogan, “Akaike's Information Criterion and Recent Developments in Information Complexity”, 2000.
  12. Tsong Yueh Chen , Fei-Ching Kuo , Robert Merkel, “On the Statistical Properties of the F-measure” 2004.
  13. Abdolreza Rasouli, Mohd Aizaini Bin Maarof, Mahboubeh Shamsi, “A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data”, 2009.
  14. Nan Ye, Kian Ming A. Chai, Wee Sun Lee, Hai Leong Chieu, “Optimizing F-Measures: A Tale of Two Approaches”,2012
  15. Jörg Sander, Xuejie Qin, Zhiyong Lu, Nan Niu, Alex Kovarsky, “Automatic Extraction of Clusters from Hierarchical Clustering Representations”,2003
  16. Eric Yi Liu, Zhishan Guo, Xiang Zhang, Vladimir Jojic and Wei Wang, “Metric Learning From Relative Comparisons by Minimizing Squared Residual”,2012
  17. E. Xing et al., “Distance metric learning with application to clustering with side-information”, 2003.


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