DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

Volumn , Issue , 2012, Pages 573-578

  Smiti, Abir ^a   Elouedi, Zied ^a  

^a UNIVERSITÉ DE TUNIS   (Tunisia)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER CENTERS; CLUSTER VALIDITY; CLUSTERING METHODS; CLUSTERING RESULTS; CLUSTERING TECHNIQUES; DATA SETS; DBSCAN ALGORITHM; DENSITY CLUSTERING; GAUSSIANS; INPUT PARAMETER; INTELLIGENT ENGINEERINGS; K-MEANS; LARGE DATASETS; LARGE DIMENSIONS; NOISY DATA; PARTITIONING ALGORITHMS; PRIORI INFORMATION; QUALITY OF CLUSTERING; SMALL CLUSTERS;

CLUSTERING ALGORITHMS;

GAUSSIAN DISTRIBUTION;

EID: 84866657447     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/INES.2012.6249802     Document Type: Conference Paper

References (15)

1. J. B. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proceeding of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, Eds., vol. 1. University of California Press, 1967, pp. 281-297.
2. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proceeding of 2nd International Conference on Knowledge Discovery and, 1996, pp. 226-231.
3. M. Dash, H. Liu, and X. Xu, "'1+1¿2': Merging distance and density based clustering," in DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, 2001. PROCEEDINGS. SEVENTH INTERNATIONAL CONFERENCE ON, 2001, pp. 32-39.
4. A. Lourenço and A. L. N. Fred, "Comparison of combination methods using Spectral Clustering Ensembles," in Proceedings of the International Workshop on Pattern Recognition in Information Systems, vol. PRIS2004, 2004, pp. 222-234.
5. H. S. Park and C. H. Jun, "A simple and fast algorithm for k-medoids clustering," International Journal Expert Systems with Applications, vol. 36, pp. 3336-3341, March 2009.
6. G. Hamerly and C. Elkan, "Learning the k in k-means," vol. 17. MIT Press, 2003.
7. E. Chandra and V. P. Anuradha, "A survey on clustering algorithms for data in spatial database management systems," International Journal of Computer Applications, vol. 24, no. 9, pp. 19-26, June 2011, published by Foundation of Computer Science.
8. T. K. Moon, "The expectation-maximization algorithm," Signal Processing Magazine, IEEE, vol. 13, no. 6, pp. 47-60, Nov. 1996.
9. R. R. S. Ramaswamy and K. Shim, "Efficient algorithms for mining outliers from large data sets," in Proceedings of the 2000 ACM SIGMOD international conference on Management of data, ser. SIGMOD '00. New York, NY, USA: ACM, 2000, pp. 427-438.
10. M. Ceccato, M. Marin, K. Mens, L. Moonen, P. Tonella, and T. Tourwe, "A qualitative comparison of three aspect mining techniques," 13th International Workshop on Program Comprehension IWPC05, no. 1, pp. 13-22, 2005.
11. A. Smiti and Z. Elouedi, "Wcoid: Maintaining case-based reasoning systems using weighting, clustering, outliers and internal cases detection," The Eleventh International on Intelligent Systems Design and Applications, ISDA 2011,, pp. 356 - 361, 2011.
12. M. Halkidi and M. Vazirgiannis, "Clustering validity assessment: Finding the optimal partitioning of a data set," in Proceedings of the 2001 IEEE International Conference on Data Mining, ser. ICDM '01, 2001, pp. 187-194.
13. A. Asuncion and D. Newman, "UCI machine learning repository. http://www.ics.uci.edu/mlearn," 2007. [Online]. Available: http://www.ics.uci.edu/mlearn
14. P. B. Nagpal and P. A. Mann, "Article: Comparative study of density based clustering algorithms," International Journal of Computer Applications, vol. 27, no. 11, pp. 44-47, August 2011, published by Foundation of Computer Science, New York, USA.
15. A. A. Yarifard and M. H. Yaghmaee, "The monitoring system based on traffic classification," World Applied Sciences Journal, vol. 5, no. 2, November 2008.