Novel approach for data stream clustering through micro-clusters shared Density
Keywords:
Data mining, data stream clustering, density-based clusteringAbstract
Clustering is the process of organizing objects into groups whose members are similar in some way and is very important technique in data mining as it has its applications spread extensively, e.g. marketing, biology, pattern recognition etc. So summarize the data stream in the real life with the online process is called as micro-cluster but it shows the density when we are combining the data in the one place. In the offline process we are using the modification clustering algorithm to re-clustering into larger cluster. For that the center of micro-cluster point as the pseudo point with density randomly calculates their weight. That density information area of micro-cluster is not preserved the online process. So used DBSTREAM, the first micro-cluster based on online clustering component capture the density between micro-cluster via shared density graph. We develop and evaluate a new method to address this problem for micro-cluster-based algorithms. The density information in this graph is then exploited for re-clustering based on actual density between adjacent micro-clusters. For that shared density graph improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro-clusters to achieve comparable results.
References
. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, “A framework for clustering evolving data streams,” in Proceedings of the International Conference on Very Large Data Bases (VLDB ’03), 2003, pp. 81–92.
. F. Cao, M. Ester, W. Qian, and A. Zhou, “Density-based clustering over an evolving data stream with noise,” in Proceedings of the 2006 SIAM International Conference on Data Mining. SIAM, 2006, pp. 328–339.
. Y. Chen and L. Tu, “Density-based clustering for real-time stream data,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2007, pp. 133–142.
. J. A. Silva, E. R. Faria, R. C. Barros, E. R. Hruschka, A. C. P. L. F. d. Carvalho, and J. a. Gama, “Data stream clustering: A survey,” ACM Computing Surveys, vol. 46, no. 1, pp. 13:1–13:31, Jul. 2013.
. A . Bifet, G. de Francisci Morales, J. Read, G. Holmes, and B. Pfahringer,” Efficient Online Evaluation of Big Data Stream Classifiers.” 2015
. H. Kremer, P. Kranen, T. Jansen, T. Seidl, A. Bifet, G. Holmes, and B. Pfahringer.” An Effective Evaluation Measure for Clustering on Evolving Data Streams.” 2011
. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’1996), 1996.
. Komkrit Udommanetanakit, Thanawin Rakthanmanon, and Kitsana Waiyamai.” Estream: Evolution-based technique for stream clustering. ” 2007
. Dimitris K. Tasoulis, Niall M. Adams, David J. Hand.” Unsupervised clustering in streaming data.” 2006
. Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, Member, IEEE, and Liadan O’Callaghan. “Clustering data streams: Theory and practice.”2013
. F. Cao, M. Ester, W. Qian, and A. Zhou, “Density-based clustering over an evolving data stream with noise,” in Proceedings of the 2006 SIAM International Conference on Data Mining. SIAM, 2006, pp. 328–339.
. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’1996), 1996, pp. 226–231.
. G. Karypis, E.-H. S. Han, and V. Kumar, “Chameleon: Hierarchical clustering using dynamic modeling,” Computer, vol. 32, no. 8, pp. 68–75, Aug. 1999. [Online]. Available:http://dx.doi.org/10.1109/2.781637
. Enakshmi Nandi1, Debabrata Sarddar “A Modified MapReduce-K-Means Clustering Based Load Distribution Method for Wireless Sensor Network in Mobile Cloud computing” ,2016
. Pritika goel, “An Improved Load Balancing Technique in Weighted Clustering Algorithm” ,2016.Available: http://www.ijcseonline.org/pdf_paper_view.php?paper_id=972&18-IJCSE-01678.pdf
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
