Random Articles
Reseach Article
A Novel Approach for PAM Clustering Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 86 - Number 17 |
| Year of Publication: 2014 |
| Authors: Faisal Bin Al Abid |
10.5120/15074-3039
|
Faisal Bin Al Abid . A Novel Approach for PAM Clustering Method. International Journal of Computer Applications. 86, 17 ( January 2014), 1-5. DOI=10.5120/15074-3039
Abstract
Existing and in recent times proposed clustering algorithms are studied and it is known that the k-means clustering method is mostly used for clustering of data due to its reduction of time complexity. But the foremost drawback of k-means algorithm is that it suffers from sensitivity of outliers which may deform the distribution of data owing to the significant values. The drawback of the k-means algorithm is resolved by k-medoids method where the novel approach uses user defined value for k. As a result, if the number of clusters is not chosen suitably, the accuracy will be minimized. Even, K-medoids algorithm does not scale well for huge data set. In order to overcome the above stated limitations, a new grid based clustering method is proposed, where time complexity of proposed algorithm is depending on the number of cells. Simulation results show that, the proposed approach has less time complexity and provides natural clustering method which scales well for large dataset.
References
- Han Jiawei and Kamber Micheline, "Data Mining Concepts and Techniques", second ed, China Machine Press, 2006.
- M. Ester, A. Frommelt, H. -P. Kriegel, and J. Sander "Spatial data mining: database primitives, algorithms and efficient DBMS support. " Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Volume 4, 2000,pp 193-216.
- Cadez I. , Smyth P. and Mannila H. "Probabilistic modeling of transactional data with applications to profiling, Visualization, and Prediction", In Proc of the7th ACM SIGKDD, 2001, San Francisco, pp. 37-46.
- Cooley R. , Mobasher B. and Srivastava J. "Data preparation for mining world wide web browsing", Journal of Knowledge Information Systems, vol 1, pp 5-32, 1999.
- A. Ben-Dor and Z. Yakhini "Clustering gene expression patterns" In Proc of the 3rd Annual International Conference on Computational Molecular Biology (RECOMB 99), 1999, Lyon, France, pp11-14.
- Faisal Bin Al Abid, M. A. Mottalib," An Accurate Grid -based PAM Clustering Method for Large Dataset", International Journal of Computer Applications (0975 – 8887) Volume 41– No. 21, March 2012
- Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, 179 (2009) 2232–2248
- A. Jain, R. Dubes. "Algorithms for Clustering Data" Prentice-Hall, EnglewoodCliffs, NJ, 1988.
- E. Koltach. "Clustering Algorithms for Spatial Databases: A Survey", Department of Computer Science, University of Maryland, 2001. Available at: http://www. cs. umd. edu/~kolatch/papers/SpatialClustering. pdf.
- W. Wang, J. Yang, and R. Muntz, "STING: a statistical information grid approach to spatial data mining", In Proc of the 23rd VLDB Conference, 1997, Athens, Greece, pp. 186-195.
- R. Ng, and J. Han, "Efficient and effective clustering methods for spatial data mining" In Proceedings of the 20th Conference on VLDB, 1994, Santiago, Chile, pp. 144-155.
- Su Youli,Yi , Guohua Chen Liu, "GK-means: An Efficient K-means Clustering Algorithm Based On Grid", School of Information Science and Engineering Lanzhou University, In Proc. Of the International symposium on Computer network and multimedia Technology (CNMT), Wuhan ,2009,pp- 1 – 4.
- L. Blake and C. J. Merz. UCI repository of machine learning databases. Department of Information and Computer Sciences, University of California, Irvine, 1998. http://www, ies. uei. edu/~mlearn/MLRepos irony, html.
Index Terms
Keywords