Abstract
In this paper, we discuss the main problems of inductive query languages and optimisation issues. We present a logic-based inductive query language and illustrate the use of aggregates and exploit a new join operator to model specific data mining tasks. We show how a fixpoint operator works for association rule mining and a clustering method. A preliminary experimental result shows that fixpoint operator outperforms SQL and Apriori methods. The results of our framework could be useful for inductive query language design in the development of inductive database systems.
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Keywords
- Association Rule
- Query Language
- Frequent Itemset
- Association Rule Mining
- Preliminary Experimental Result
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Raedt, L.D.: A perspective on inductive databases. SIGKDD Explorations 4, 69–77 (2002)
Zaniolo, C.: Mining databases and data streams with query languages and rules. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 24–37. Springer, Heidelberg (2006)
Imielinski, T., Virmani, A.: Msql: A query language for database mining. Data Mining and Knowledge Discovery 2(4), 373–408 (1999)
Han, J., Fu, Y., Koperski, K., Wang, W., Zaiane, O.: Dmql: A data mining query language for relational databases. In: Proceedings of ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (1996)
Meo, R., Psaila, G., Ceri, S.: An extension to sql for mining association rules. Data mining and knowledge discovery 2(2), 195–224 (1998)
Sarawagi, S., Thomas, S., Agrawal, R.: Integrating association rule mining with relational database systems: alternatives and implications. Data mining and knowledge discovery 4, 89–125 (2000)
Giannotti, F., Manco, G., Turini, F.: Towards a logic query language for data mining. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 76–94. Springer, Heidelberg (2004)
Jamil, H.M.: Bottom-up association rule mining in relational databases. Journal of Intelligent Information Systems, 1–17 (2002)
Masson, C., Robardet, C., Boulicaut, J.-F.: Optimizing subset queries: a step towards sql-based inductive databases for itemsets. In: Proceedings of the 2004 ACM symposium on applied computing, pp. 535–539 (2004)
Liu, H.-C., Yu, J.: Algebraic equivalences of nested relational operators. Information Systems 30(3), 167–204 (2005)
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Liu, HC., Yu, J.X., Zeleznikow, J., Guan, Y. (2007). A Logic-Based Approach to Mining Inductive Databases. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4487. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72584-8_35
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DOI: https://doi.org/10.1007/978-3-540-72584-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72583-1
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