Abstract
Evolution-based stream clustering method supports the monitoring and the change detection of clustering structures. E-Stream is an evolution-based stream clustering method that supports different types of clustering structure evolution which are appearance, disappearance, self-evolution, merge and split. This paper presents HUE-Stream which extends E-Stream in order to support uncertainty in heterogeneous data. A distance function, cluster representation and histogram management are introduced for the different types of clustering structure evolution. We evaluate effectiveness of HUE-Stream on real-world dataset KDDCup 1999 Network Intruision Detection. Experimental results show that HUE-Stream gives better cluster quality compared with UMicro.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C.: On High Dimensional Projected Clustering of Uncertain Data Streams. In: IEEE 25th International Conference on Data Engineering, ICDE 2009, March 29 - April 2, pp. 1152–1154 (2009)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. Paper presented at the Proceedings of the 29th International Conference on Very Large Data Bases, Berlin, Germany, vol. 29 (2003)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for projected clustering of high dimensional data streams. Paper presented at the Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, vol. 30 (2004)
Aggarwal, C.C., Yu, P.S.: A Framework for Clustering Uncertain Data Streams. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, April 7-12, pp. 150–159 (2008)
Chen, Z., Ming, G., Aoying, Z.: Tracking High Quality Clusters over Uncertain Data Streams. In: IEEE 25th International Conference on Data Engineering, ICDE 2009, March 29 - April 2, pp. 1641–1648 (2009)
Qin, B., Xia, Y., Prabhakar, S., Tu, Y.: A Rule-Based Classification Algorithm for Uncertain Data. Paper presented at the Proceedings of the 2009 IEEE International Conference on Data Engineering (2009)
Udommanetanakit, K., Rakthanmanon, T., Waiyamai, K.: E-Stream: Evolution-Based Technique for Stream Clustering. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaïane, O.R. (eds.) ADMA 2007. LNCS (LNAI), vol. 4632, pp. 605–615. Springer, Heidelberg (2007)
Kosonpothisakun, P., Kangkachit, T., Waiyamai, K.: E-Stream++: Stream clustering technique for supporting numerical and categorical data. Paper presented at the Proceedings of the 13th National Computer Science and Engineering Conference, Bangkok, Thailand (2009)
Yang, C., Zhou, J.: HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream. Paper presented at the Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)
Huang, G.Y., Liang, D.P., Hu, C.Z., Ren, J.D.: An algorithm for clustering heterogeneous data streams with uncertainty. Paper presented at the Proceedings of International Conference on Machine Learning and Computing, Qingdao, China (2010)
The network intrusion detection data set, http://archive.ics.uci.edu/ml/datasets/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meesuksabai, W., Kangkachit, T., Waiyamai, K. (2011). HUE-Stream: Evolution-Based Clustering Technique for Heterogeneous Data Streams with Uncertainty. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-25856-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25855-8
Online ISBN: 978-3-642-25856-5
eBook Packages: Computer ScienceComputer Science (R0)