Abstract
There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domain, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions. Applying techniques for static datasets is not practical for data streams. In this paper we propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree), which finds rare rules in a data stream environment using a sliding window, and show that it is faster than current approaches.
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
Adda, M., Wu, L., Feng, Y.: Rare itemset mining. In: Proceedings of the Sixth International Conference on Machine Learning and Applications, ICMLA 2007, pp. 73–80. IEEE Computer Society, Washington, DC (2007)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487–499 (1994)
Cheng, J., Ke, Y., Ng, W.: Maintaining frequent closed itemsets over a sliding window. J. Intell. Inf. Syst. 31, 191–215 (2008)
Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Moment: Maintaining closed frequent itemsets over a stream sliding window. In: Proceedings of the Fourth IEEE International Conference on Data Mining, ICDM 2004, pp. 59–66. IEEE Computer Society, Washington, DC (2004)
Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Catch the moment: maintaining closed frequent itemsets over a data stream sliding window. Knowl. Inf. Syst. 10, 265–294 (2006)
Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining stream statistics over sliding windows (extended abstract). In: Proceedings of the Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2002, pp. 635–644. Society for Industrial and Applied Mathematics, Philadelphia (2002)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 1–12. ACM, New York (2000)
Koh, Y.S., Rountree, N.: Finding Sporadic Rules Using Apriori-Inverse. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 97–106. Springer, Heidelberg (2005)
Lee, C.H., Lin, C.R., Chen, M.S.: Sliding window filtering: an efficient method for incremental mining on a time-variant database. Information Systems 30(3), 227–244 (2005)
Leung, C.K.S., Khan, Q.I.: Dstree: A tree structure for the mining of frequent sets from data streams. In: Proceedings of the Sixth International Conference on Data Mining, ICDM 2006, pp. 928–932. IEEE Computer Society, Washington, DC (2006)
Li, H.F., Lee, S.Y.: Mining frequent itemsets over data streams using efficient window sliding techniques. Expert Syst. Appl. 36, 1466–1477 (2009)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341 (1999)
Mozafari, B., Thakkar, H., Zaniolo, C.: Verifying and mining frequent patterns from large windows over data streams. In: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, pp. 179–188. IEEE Computer Society, Washington, DC (2008), http://dl.acm.org/citation.cfm?id=1546682.1547157
Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. In: Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007, vol. 1, pp. 305–312. IEEE Computer Society, Washington, DC (2007)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Sliding window-based frequent pattern mining over data streams. Information Sciences 179(22), 3843–3865 (2009)
Troiano, L., Scibelli, G., Birtolo, C.: A fast algorithm for mining rare itemsets. In: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, pp. 1149–1155. IEEE Computer Society, Washington, DC (2009)
Tsang, S., Koh, Y.S., Dobbie, G.: RP-Tree: Rare Pattern Tree Mining. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 277–288. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, D., Koh, Y.S., Dobbie, G. (2012). Rare Pattern Mining on Data Streams. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-32584-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32583-0
Online ISBN: 978-3-642-32584-7
eBook Packages: Computer ScienceComputer Science (R0)