Ching-Ming Chao This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Guan-Lin Chao2 1Department of Computer Science and Information Management, Soochow University, Taipei, Taiwan 100, R.O.C.
2Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, R.O.C.
Received:
September 9, 2010
Accepted:
March 11, 2011
Publication Date:
December 1, 2011
Download Citation:
||https://doi.org/10.6180/jase.2011.14.4.10
Data stream mining has attracted much research attention from the data mining community. With the advance of wireless networks and mobile devices, the concept of ubiquitous data mining has been proposed. However, mobile devices are resource-constrained, which makes data stream mining a greater challenge. In this paper, we propose the RA-HCluster algorithm that can be used in mobile devices for clustering stream data. It adapts algorithm settings and compresses stream data based on currently available resources, so that mobile devices can continue with clustering at acceptable accuracy even under low memory resources. Experimental results show that not only is RA-HCluster more accurate than RA-VFKM, it is able to maintain a low and stable memory usage.ABSTRACT
Keywords:
Data Mining, Data Streams, Clustering, Ubiquitous Data Mining
[1] Kargupta, H., Park, B. H., Pittie, S., Liu, L., Kushraj, D. and Sarkar, K., “MobiMine: Monitoring the Stock Market from a PDA,” ACM SIGKDD Explorations Newsletter, Vol. 3, pp. 37 46 (2002). [2] Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M. and Handy, D., “VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring,” Proceedings of the 4th SIAM International Conference on Data Mining, pp. 300 311 (2004). [3] Babcock, B., Babu, S., Motwani, R. and Widom, J., “Models and Issues in Data Stream Systems,” Proceedings of the 21st ACM SIGMOD Symposium on Principles of Database Systems, Madison, pp. 1 16 (2002). [4] Golab, L. and Ozsu, T. M., “Issues in Data Stream Management,” ACM SIGMOD Record, Vol. 32, pp. 5 14 (2003). [5] Chao, C. M. and Chao, G. L., “Resource-Aware High Quality Clustering in Ubiquitous Data Streams,” in Proceedings of the 13th International Conference on Enterprise Information Systems, Beijing, China (2011). [6] Gaber, M. M., Zaslavsky, A. and Krishnaswamy, S., “Towards an Adaptive Approach for Mining Data Streams in Resource Constrained Environments,” Proceedings of the International Conference on Data Warehousing and Knowledge Discovery, pp. 189 198 (2004). [7] Aggarwal, C. C., Han, J., Wang, J. and Yu, P. S., “A Framework for Clustering Evolving Data Streams,” Proceedings of the 29th International Conference on Very Large Data Bases, pp. 81 92 (2003). [8] Gaber, M. M., Krishnaswamy, S. and Zaslavsky, A., “Ubiquitous Data Stream Mining,” Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 81 90 (2004). [9] Shah, R., Krishnaswamy, S. and Gaber, M. M., “Resource-Aware Very Fast K-Means for Ubiquitous Data Stream Mining,” Proceedings of the 2nd International Workshop on Knowledge Discovery in Data Streams, pp. 40 50 (2005). [10] Gaber, M. M. and Yu, P. S., “A Framework for Resource-Aware Knowledge Discovery in Data Streams: A Holistic Approach with Its Application to Clustering,” Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 649 656 (2006). [11] Dai, B. R., Huang, J. W., Yeh, M. Y. and Chen, M. S., “Adaptive Clustering for Multiple Evolving Streams,” IEEE Transactions on Knowledge and Data Engineering, Vol. 18, pp. 1166 1180 (2006).REFERENCES
