Abstract
Most association rule mining techniques concentrate on finding frequent rules. However, rare association rules are in some cases more interesting than frequent association rules since rare rules represent unexpected or unknown associations. All current algorithms for rare association rule mining use an Apriori level-wise approach which has computationally expensive candidate generation and pruning steps. We propose RP-Tree, a method for mining a subset of rare association rules using a tree structure, and an information gain component that helps to identify the more interesting association rules. Empirical evaluation using a range of real world datasets shows that RP-Tree itemset and rule generation is more time efficient than modified versions of FP-Growth and ARIMA, and discovers 92-100% of all the interesting rare association rules. Additional evaluation using synthetic datasets also shows that RP-Tree is more efficient, in addtion to showing how the execution time of RP-Tree changes with transaction length and rare-item itemset size.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
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)
Sotiris, K., Dimitris, K.: Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering 32 (1), 71–82 (2006)
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)
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)
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. 01, pp. 305–312. IEEE Computer Society, Washington, DC (2007)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: 3rd Intl. Conf. on Knowledge Discovery and Data Mining, pp. 283–286. AAAI Press (1997)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328 (1996)
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)
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)
Mitchell, T.M.: Machine Learning, pp. 57–60. McGraw-Hill (1997)
Wu, T., Chen, Y., Han, J.: Association Mining in Large Databases: A Re-examination of Its Measures. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 621–628. Springer, Heidelberg (2007)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Tsang, S., Koh, Y.S., Dobbie, G. (2013). Finding Interesting Rare Association Rules Using Rare Pattern Tree. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII. Lecture Notes in Computer Science, vol 7790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37574-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-37574-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37573-6
Online ISBN: 978-3-642-37574-3
eBook Packages: Computer ScienceComputer Science (R0)