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Performance Comparison of Incremental K-means and Incremental DBSCAN Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 27 - Number 11 |
| Year of Publication: 2011 |
| Authors: Sanjay Chakraborty, Prof. N.K.Nagwani, Lopamudra Dey |
10.5120/3346-4611
|
Sanjay Chakraborty, Prof. N.K.Nagwani, Lopamudra Dey . Performance Comparison of Incremental K-means and Incremental DBSCAN Algorithms. International Journal of Computer Applications. 27, 11 ( August 2011), 14-18. DOI=10.5120/3346-4611
Abstract
Incremental K-means and DBSCAN are two very important and popular clustering techniques for today’s large dynamic databases (Data warehouses, WWW and so on) where data are changed at random fashion. The performance of the incremental K-means and the incremental DBSCAN are different with each other based on their time analysis characteristics. Both algorithms are efficient compare to their existing algorithms with respect to time, cost and effort. In this paper, the performance evaluation of incremental DBSCAN clustering algorithm is implemented and most importantly it is compared with the performance of incremental K-means clustering algorithm and it also explains the characteristics of these two algorithms based on the changes of the data in the database. This paper also explains some logical differences between these two most popular clustering algorithms. This paper uses an air pollution database as original database on which the experiment is performed.
References
- Chakraborty, S. and Nagwani, N.K., “Analysis and study of incremental K-Means clustering algorithm”,Communication in Computer and Information Science, 1,Volume 169, High Performance Architecture and Grid Computing, Part 2, Pages 338-341, 2011.
- Chakraborty,S. and Nagwani, N.K., “Analysis and study of Incremental DBSCAN clustering algorithm ”, IJECBS, vol.1, 2011.
- Chakraborty,S.and Nagwani,N.K.,“Performance evaluation of incremental K-means clustering algorithm ”, IIJDWM vol.1, 2011, pp-54-59.
- Mumtaz, K. and Dr. K. Duraiswam , “An analysis on Density based clustering of multi dimensional Spatial data ”, IJCSE, vol.1 ,No 1, pp.8-12.
- Karahoc,A.and Kara,A.,“Comparing clustering techniques for telecom churn management”, Proceedings of the 5th WSEAS International Conference on Telecommunications and Informatics, pp281-286, May 27-29, 2006.
- Mumtaz, K. and Dr. K. Duraiswamy, “A Novel Density based improved k-means Clustering Algorithm– Dbkmeans ”, IJCSE, Vol. 02, No. 02, pp213-218, 2010.
- Xiang Li, Rahul Ramachandran, Sunil Movva and Sara Graves, “ Storm Clustering for Data-driven Weather Forecasting ”, International conference in University of Alabama in Huntsville.
- Jeffrey ,E., Arlitt, M., Mahanti, A., “Traffic Classification Using Clustering Algorithms” , SIGCOMM’06 Workshops ACM-159593417, 0/06/0009,September 1115,2006.
- Dunham, M.H., 2003: Data Mining: Introductory And Advanced Topics, New Jersey: Prentice Hall,450.
- Han and Kamber, J. and M.,2006: chapter-7, “cluster analysis ”, Data Mining concepts and techniques, Diane Cerra, 383-464.
- Kantardzic, 2003: M. Data Mining: concepts, models, method, And algorithms, New Jersey: IEEE press.
- Kanungo and Mount, T.and D.M.: An Efficient k-Means Clustering Algorithm: Analysis and implentation, IEEE transaction vol. 24, No. 7,2002.
- Derya Birant and Alp Kut, “ TDBSCAN: An algorithm for Clustering spatial temporal data”, Dokuz Eyu University, 35100 Izmir, Turkey,13 march 2006.
- CHEN, N., CHEN A.,ZHOU Long-xiang,“An Incremental Grid, Density-Based Clustering Algorithm”, Journal of Software Vol.13 No.1,2002.
- Ester, M., Kriegel, H., Sander, J, Wimmer, M., Xiaowei Xu, “Incremental clustering for mining in a data ware Housing”, University of Munich Oettingenstr.67, D-80538 München, Germany.
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