Abstract
Mining sequential patterns is to discover sequential purchasing behaviors for most customers from a large amount of customer transactions. Past transaction data can be analyzed to discover customer purchasing behaviors. However, the size of the transaction database can be very large. It is very time consuming to find all the sequential patterns from a large database, and users may be only interested in some items. Moreover, the criteria of the discovered sequential patterns for the user requirements may not be the same. Many uninteresting sequential patterns for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the sequential patterns to be discovered. Also, an efficient data mining technique is proposed to extract the sequential patterns according to the users’ requests.
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References
Agrawal, R., et al.: Fast Algorithm for Mining Association Rules. In: Proceedings of International Conference on Very Large Data Bases (VLDB), pp. 487–499 (1994)
Agrawal, R., et al.: Mining Sequential Patterns. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 3–14 (1995)
Agrawal, R., Srikant, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Pei, J., Han, J., et al.: PrefixSpan: Mining sequential patterns efficiently by prefixprojected pattern growth. In: Proceedings of the International Conference on Data Engineering (ICDE), Heidelberg, Germany (April 2001)
Yen, S.J., Lee, Y.S.: An Efficient Data Mining Technique for Discovering Interesting Sequential Patterns. In: Proceedings of the International Conference on Data Mining (ICDM), pp. 663–664 (2001)
Yen, S.J., Lee, Y.S.: Mining Interesting Association Rules: A Data Mining Language. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 172–176. Springer, Heidelberg (2002)
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Yen, SJ., Lee, YS. (2004). Mining Sequential Patterns with Item Constraints. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_38
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DOI: https://doi.org/10.1007/978-3-540-30076-2_38
Publisher Name: Springer, Berlin, Heidelberg
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