The week's pick
Random Articles
Reseach Article
An Optimised Density Based Clustering Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 6 - Number 9 |
| Year of Publication: 2010 |
| Authors: J. Hencil Peter, A. Antonysamy |
10.5120/1102-1445
|
J. Hencil Peter, A. Antonysamy . An Optimised Density Based Clustering Algorithm. International Journal of Computer Applications. 6, 9 ( September 2010), 20-25. DOI=10.5120/1102-1445
Abstract
The DBSCAN [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. As the original DBSCAN algorithm uses the distance measures to compute the distance between objects, it consumes so much processing time and its computation complexity comes as O (N2). In this paper we have proposed a new algorithm to improve the performance of DBSCAN algorithm. The existing algorithms A Fast DBSCAN Algorithm[6] and Memory effect in DBSCAN algorithm[7] has been combined in the new solution to speed up the performance as well as improve the quality of the output. As the RegionQuery operation takes long time to process the objects, only few objects are considered for the expansion and the remaining missed border objects are handled differently during the cluster expansion. Eventually the performance analysis and the cluster output show that the proposed solution is better to the existing algorithms.
References
- Ester M., Kriegel H.-P., Sander J., and Xu X. (1996) “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise” In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96), Portland: Oregon, pp. 226-231
- J. Han and M. Kamber, Data Mining Concepts and Techniques. Morgan Kaufman, 2006.
- G. Karypis, E. H. Han, and V. Kumar, “CHAMELEON: A hierarchical clustering algorithm using dynamic modeling,” Computer, vol. 32, no. 8, pp. 68–75, 1999.
- M. Ankerst, M. Breunig, H. P. Kriegel, and J. Sander, “OPTICS: Ordering Objects to Identify the Clustering Structure, Proc. ACM SIGMOD,” in International Conference on Management of Data, 1999, pp. 49–60.
- A. Hinneburg and D. Keim, “An efficient approach to clustering in large multimedia data sets with noise,” in 4th International Conference on Knowledge Discovery and Data Mining, 1998, pp. 58–65.
- SHOU Shui-geng, ZHOU Ao-ying JIN Wen, FAN Ye and QIAN Wei-ning.(2000) "A Fast DBSCAN Algorithm" Journal of Software: 735-744.
- Li Jian; Yu Wei; Yan Bao-Ping; , "Memory effect in DBSCAN algorithm," Computer Science & Education, 2009. ICCSE '09. 4th International Conference on , vol., no., pp.31-36, 25-28 July 2009.
Index Terms
Keywords