• العربية
    • English
  • English 
    • العربية
    • English
  • Login
Home
Publisher PoliciesTerms of InterestHelp Videos
Submit Thesis
IntroductionIUGSpace Policies
JavaScript is disabled for your browser. Some features of this site may not work without it.
View Item 
  •   Home
  • Faculty of Engineering
  • Staff Publications- Faculty of Engineering
  • View Item
  •   Home
  • Faculty of Engineering
  • Staff Publications- Faculty of Engineering
  • View Item

Please use this identifier to cite or link to this item:

http://hdl.handle.net/20.500.12358/24520
TitleA dynamic method for discovering density varied clusters
Untitled
Abstract

Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes, sizes that differ in local density. We introduce new algorithm Dynamic Method DBSCAN (DMDBSCAN). It selects several values of the radius of a number of objects (Eps) for different densities according to a k-dist plot. For each value of Eps, DBSCAN algorithm is adopted in order to make sure that all the clusters with respect to the corresponding density are clustered. For the next process, the points that have been clustered are ignored, which avoids marking both denser areas and sparser ones as one cluster. Experimental results are obtained from artificial data sets and UCI real data sets. The final results show that our algorithm get a good results with respect to the original DBSCAN and DVBSCAN algorithms.

Authors
Elbatta, Mohammed TH
Ashour, Wesam M.
TypeJournal Article
Date2013
Subjects
dbscan
k-dist
density different cluster
variance density
Published inInternational Journal of Signal Processing, Image Processing and Pattern Recognition
SeriesVolume: 6, Number: 1
PublisherHindawi Limited
Citation
Item linkItem Link
License
Collections
  • Staff Publications- Faculty of Engineering [908]
Files in this item
Ashour, Wesam M._14.pdf564.2Kb
Thumbnail

The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback
 

 

Browse

All of IUGSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsSupervisorsThis CollectionBy Issue DateAuthorsTitlesSubjectsSupervisors

My Account

LoginRegister

Statistics

View Usage Statistics

The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback