Enhancing of DBSCAN based on Sampling and Density-based Separation | ||
| Iraqi Journal for Computers and Informatics ijci | ||
| Article 1, Volume 42, Issue 1, December 2016, Pages 38-47 | ||
| Authors | ||
| Safaa O. Al-mamory; iqIsraa Saleh Kamil | ||
| Abstract | ||
| DBSCAN (Density-Based Clustering of Applications with Noise )is one of the attractive algorithms among density-based clustering algorithms. It characterized by its ability to detect clusters of various sizes and shapes with the presence of noise, but its performance degrades when data have different densities .In this paper, we proposed a new technique to separate data based on its density with a new sampling technique , the purpose of these new techniques is for getting data with homogenous density .The experimental results on synthetic data and real world data show that the new technique enhanced the clustering of DBSCAN to large extent. | ||
| Keywords | ||
| DBSCAN; Sampling; density; Based; Separation | ||
|
Statistics Article View: 76 PDF Download: 62 |
||