Data partitioning technique to enhance DBSCAN clustering algorithm
Joint Authors
kamil, Isra Salih
al-Mamuri, Safa O.
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 25, Issue 2 (30 Jun. 2017), pp.329-340, 12 p.
Publisher
Publication Date
2017-06-30
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Among density- based clustering techniques ,DBSCAN is a typical one because it can detect clusters with widely different shapes and sizes, but it fails to find clusters with different densities and for that we propose a new technique to enhance the performance of DBSCAN on data with different densities ,the new solution contains two novel tech¬niques ,one is the separation (partitioning ) technique that separate data into sparse and dense regions, and the other is the sampling technique that produce data with only one density distribution.
the experimental results on synthetic data show that the new tech¬nique has a clustering.
American Psychological Association (APA)
al-Mamuri, Safa O.& kamil, Isra Salih. 2017. Data partitioning technique to enhance DBSCAN clustering algorithm. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 25, no. 2, pp.329-340.
https://search.emarefa.net/detail/BIM-1169339
Modern Language Association (MLA)
al-Mamuri, Safa O.& kamil, Isra Salih. Data partitioning technique to enhance DBSCAN clustering algorithm. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 25, no. 2 (2017), pp.329-340.
https://search.emarefa.net/detail/BIM-1169339
American Medical Association (AMA)
al-Mamuri, Safa O.& kamil, Isra Salih. Data partitioning technique to enhance DBSCAN clustering algorithm. Journal of Babylon University : Journal of Applied and Pure Sciences. 2017. Vol. 25, no. 2, pp.329-340.
https://search.emarefa.net/detail/BIM-1169339
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 339-340
Record ID
BIM-1169339