Abstract
Researchers convincingly argue that the ability to declaratively mine and analyze relational databases using SQL for decision support is a critical requirement for the success of the acclaimed data mining technology. Although there have been several encouraging attempts at developing methods for data mining using SQL, simplicity and efficiency still remain significant impediments for further development. In this article, we propose a significantly new approach and show that any object relational database can be mined for association rules without any restructuring or preprocessing using only basic SQL3 constructs and functions, and hence no additional machineries are necessary. In particular, we show that the cost of computing association rules for a given database does not depend on support and confidence thresholds. More precisely, the set of large items can be computed using one simple join query and an aggregation once the set of all possible meets (least fixpoint) of item set patterns in the input table is known. We believe that this is an encouraging discovery especially compared to the well known SQL based methods in the literature. Finally, we capture the functionality of our proposed mining method in a mine by SQL3 operator for general use in any relational database.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)
Beeri, C., Ramakrishnan, R.: On the power of magic. In: ACM PODS, pp. 269–283 (1987)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlations. In: Proc. ACM SIGMOD, pp. 265–276 (1997)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. ACM SIGMOD, pp. 1–12 (2000)
Jamil, H.M.: Mining first-order knowledge bases for association rules. In: Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Dallas, Texas, pp. 218–227. IEEE Press, Los Alamitos (2001)
Jamil, H.M.: A new indexing scheme for set-valued keys. Technical report, Department of Computer Science, MSU, USA (June 2001)
Jamil, H.M.: Ad hoc association rule mining as SQL3 queries. In: Proceedings of the IEEE International Conference on Data Mining, San Jose, California, pp. 609–612. IEEE Press, Los Alamitos (2001)
Jamil, H.M.: On the equivalence of top-down and bottom-up data mining in relational databases. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 41–50. Springer, Heidelberg (2001)
Jamil, H.M.: Bottom-up association rule mining in relational databases. Journal of Intelligent Information Systems 19(2), 191–206 (2002)
Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, I.: Finding interesting rules from large sets of discovered association rules. In: CIKM, pp. 401–407 (1994)
Korn, F., Labrinidis, A., Kotidis, Y., Faloutsos, C.: Ratio rules: A new paradigm for fast, quantifiable data mining. In: Proc. of 24th VLDB, pp. 582–593 (1998)
Lent, B., Swami, A.N., Widom, J.: Clustering association rules. In: Proc. of the 3th ICDE, pp. 220–231 (1997)
Meo, R., Psaila, G., Ceri, S.: A new SQL-like operator for mining association rules. In: Proc. of 22nd VLDB, pp. 122–133 (1996)
Meo, R., Psaila, G., Ceri, S.: An extension to SQL for mining association rules. DMKD 2(2), 195–224 (1998)
Mumick, I.S., Pirahesh, H.: Implementation of magic-sets in a relational database system. In: ACM SIGMOD, pp. 103–114 (1994)
Netz, A., Chaudhuri, S., Bernhardt, J., Fayyad, U.M.: Integration of data mining with database technology. In: Proceedings of 26th VLDB, pp. 719–722 (2000)
Netz, A., Chaudhuri, S., Fayyad, U.M., Bernhardt, J.: Integrating data mining with SQL databases. In: IEEE ICDE (2001)
Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained association rules. In: Proc. ACM SIGMOD, pp. 13–24 (1998)
Park, J.S., Chen, M.-S., Yu, P.S.: An effective hash based algorithm for mining association rules. In: Proc. ACM SIGMOD, pp. 175–186 (1995)
Rajamani, K., Cox, A., Iyer, B., Chadha, A.: Efficient mining for association rules with relational database systems. In: IDEAS, pp. 148–155 (1999)
Sarawagi, S., Thomas, S., Agrawal, R.: Integrating mining with relational database systems: Alternatives and implications. In: Proc. ACM SIGMOD, pp. 343–354 (1998)
Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: Proc of 21th VLDB, pp. 432–444 (1995)
Shenoy, P., Haritsa, J.R., Sudarshan, S., Bhalotia, G., Bawa, M., Shah, D.: Turbo-charging vertical mining of large databases. In: ACM SIGMOD, pp. 22–33 (2000)
Silberschatz, A., Korth, H.F., Sudarshan, S.: Database System Concepts, 3rd edn. McGraw-Hill, New York (1996)
Thomas, S., Sarawagi, S.: Mining generalized association rules and sequential patterns using SQL queries. In: KDD, pp. 344–348 (1998)
Ullman, J.D.: Principles of Database and Knowledge-base Systems, Part I & II. Computer Science Press (1988)
Zaki, M.J.: Generating non-redundant association rules. In: Proc. of the 6th ACM SIGKDD Intl. Conf., Boston, MA (August 2000)
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Jamil, H.M. (2004). Declarative Data Mining Using SQL3. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds) Database Support for Data Mining Applications. Lecture Notes in Computer Science(), vol 2682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44497-8_3
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DOI: https://doi.org/10.1007/978-3-540-44497-8_3
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