International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 4, No. 2, 8 Mar. 2012
Cover page and Table of Contents: PDF (size: 689KB)
Finding Within Cluster Dense Regions Using Distance Based Technique
Full Text (PDF, 689KB), PP.42-48
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Author(s)
Wesam Ashour
1,*
Motaz Murtaja
1
Index Terms
Data clustering, density-based, DBSCAN, glory point, different density
Abstract
One of the main categories in Data Clustering is density based clustering. Density based clustering techniques like DBSCAN are attractive because they can find arbitrary shaped clusters along with noisy outlier. The main weakness of the traditional density based algorithms like DBSCAN is clustering the different density level data sets. DBSCAN calculations done according to given parameters applied to all points in a data set, while densities of the data set clusters may be totally different. The proposed algorithm overcomes this weakness of the traditional density based algorithms. The algorithm starts with partitioning the data within a cluster to units based on a user parameter and compute the density for each unit separately. Consequently, the algorithm compares the results and merges neighboring units with closer approximate density values to become a new cluster. The experimental results of the simulation show that the proposed algorithm gives good results in finding clusters for different density cluster data set.
Cite This Paper
Wesam Ashour, Motaz Murtaja, "Finding Within Cluster Dense Regions Using Distance Based Technique", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.2, pp.42-48, 2012. DOI:10.5815/ijisa.2012.02.05
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