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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access December 28, 2012

DRL-Prefixspan: A novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns

  • Aloysius George EMAIL logo and D. Binu
From the journal Open Computer Science


Discovering sequential patterns is a rather well-studied area in data mining and has been found many diverse applications, such as basket analysis, telecommunications, etc. In this article, we propose an efficient algorithm that incorporates constraints and promotion-based marketing scenarios for the mining of valuable sequential patterns. Incorporating specific constraints into the sequential mining process has enabled the discovery of more user-centered patterns. We move one step ahead and integrate three significant marketing scenarios for mining promotion-oriented sequential patterns. The promotion-based market scenarios considered in the proposed research are 1) product Downturn, 2) product Revision and 3) product Launch (DRL). Each of these scenarios is characterized by distinct item and adjacency constraints. We have developed a novel DRL-PrefixSpan algorithm (tailored form of the PrefixSpan) for mining all length DRL patterns. The proposed algorithm has been validated on synthetic sequential databases. The experimental results demonstrate the effectiveness of incorporating the promotion-based marketing scenarios in the sequential pattern mining process.

[1] Agrawal R., Srikant R., Mining Sequential Patterns, In: Proceedings of the International Conference on Data Engineering. (ICDE’ 95), 3–14, 1995 Search in Google Scholar

[2] Antunes C., Oliveira A.L., Generalization of Pattern-growth Methods for Sequential Pattern Mining with Gap Constraints, Lect. Notes Comput. Sci., 2734, 239–251, 2003 in Google Scholar

[3] Antunes C., Oliveira A.L., Sequential Pattern Mining With Approximated Constraints, In: Proceedings of the International Conference on Applied Computing, 131–138, 2004 Search in Google Scholar

[4] Ayres J., Flannick J., Gehrke J., T Y., Sequential pattern mining using a bitmap representation, In: Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD’02), 429–435, 2002 10.1145/775047.775109Search in Google Scholar

[5] Bayardo R.J., The Many Roles of Constraints in Data Mining, SIGKDD Explorations, 4, i–ii, 2002 Search in Google Scholar

[6] Bollmann-Sdorra P., Hafez A.M., Raghavan V.V., A Theoretical Framework for Association Mining based on the Boolean Retrieval Model, In: Proceedings of the International conference on Data warehousing and knowledge discovery, 2114, 21–30, 2001 in Google Scholar

[7] Chen M.-S., Han J., Yu P.S., Data Mining: An Overview from Database Perspective, IEEE Trans. Knowl. Data Eng., 8, 866–883, 1996 in Google Scholar

[8] Chen Y.-L., Hu Y.-H., Constraint-based sequential pattern mining: The consideration of recency and compactness, Decis. Support Systems, 42, 1203–1215, 2006 in Google Scholar

[9] Chen Y.-L., Kuo M.-H., Wu S.-Y., Tangc K., Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data, Electron. Commerce Res. Appl., 8, 241–251, 2009 in Google Scholar

[10] Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurasamy R., Advances in Knowledge Discovery and Data Mining (MIT Press, 1996) Search in Google Scholar

[11] Garofalakis M., Rastogi R., Shim K., Mining Sequential Patterns with Regular Expression Constraints, IEEE Trans. Knowl. Data Eng., 14, 530–552, 2002 in Google Scholar

[12] Ghatari A.R., Mohamadi N., Honarmand A., Ahmadi P., Mohamadi N., Recognizing & Prioritizing Of Critical Success Factors (CSFs) On Data Mining Algorithm’s Implementation In Banking Industry: Evidence From Banking Business System”, In: Proceedings of EABR & TLC Conference, 2009 Search in Google Scholar

[13] Han J., Kamber M., Data Mining: Concepts and Techniques (Morgan Kaufmann, 2000) Search in Google Scholar

[14] Hu Y.H., Huang T.Ch-K., Yang H.-R., Chen Y.-L., On mining multi-time-interval sequential patterns, Data & Knowledge Engineer., 68, 1112–1127, 2009 in Google Scholar

[15] Huang J.-W., Tseng Ch.-Y., Ou J.-Ch., Chen M.-S., A General Model for Sequential Pattern Mining with a Progressive Database, IEEE Trans. Knowl. Data Eng., 20, 2008 10.1109/TKDE.2008.37Search in Google Scholar

[16] Kantardzic M., Data Mining: Concepts, Models, Methods, and Algorithms (John Wiley & Sons Inc., 2002) Search in Google Scholar

[17] Li Y.-Ch., Yeh J.-S., Chang Ch.-Ch., Isolated items discarding strategy for discovering high utility itemsets, Data & Knowledge Eng., 64, 198–217, January 2008 in Google Scholar

[18] Lin M.-Y., Lee S.-Y., Efficient mining of sequential patterns with time constraints by delimited pattern growth, Knowl. Inf. Syst., 7, 499–514, 2005 in Google Scholar

[19] Lin M.-Y., Lee S.-Y., Interactive Sequence Discovery by Incremental Mining, Int. J. Inf. Sci., 165, 187–205, 2004 10.1016/j.ins.2003.09.021Search in Google Scholar

[20] Maimon O.Z., Rokach L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications (World Scientific Publishing Company, 2005) 10.1142/5686Search in Google Scholar

[21] Masseglia F., Poncelet P., Teisseire M., Efficient Mining Sequential Patterns with Time Constraints: Reducing the Combinations, Expert Syst. Appl., 36, 2677–2690, 2009 in Google Scholar

[22] Masseglia F., Poncelet P., Teisseire M., Incremental mining of sequential patterns in large databases, Data & Knowledge Engineer., 46, 97–121, 2003 in Google Scholar

[23] Morzy T., Wojciechowski M., Zakrzewicz M., Efficient Constraint-Based Sequential Pattern Mining Using Dataset Filtering Techniques, In: Proceedings of the Baltic Conference, Baltic DB&IS 2002, 1, 213–224, 2002 10.1007/978-94-015-9978-8_23Search in Google Scholar

[24] Orlando S., Perego R., Silvestri C., A new algorithm for gap constrained sequence mining, In: Proceedings of the ACM Symposium on Applied Computing, 540–547, 2004 10.1145/967900.968014Search in Google Scholar

[25] Pei J. et al., Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach, IEEE Trans. Knowl. Data Eng., 16, 1424–1440, 2004 in Google Scholar

[26] Pei J. et al., PrefixSpan: Mining sequential patterns by prefix-projected growth, ICDE, 215–224, 2001 Search in Google Scholar

[27] Pei J., Han J., Wang W., Constraint-based sequential pattern mining: the pattern-growth methods, J. Intell. Inf. Syst., 28, 133–160, 2007 in Google Scholar

[28] Pei J., Han J., Wang W., Mining sequential patterns with constraints in large databases, In: Proceedings of the 11th International Conference on Information and Knowledge Management, 18–25, 2002 10.1145/584792.584799Search in Google Scholar

[29] Qiankun Zhao, Bhowmick S.S., Sequential Pattern Mining: A Survey” Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003118, 2003 Search in Google Scholar

[30] Ren J., Tian H., Shiyong Lv, Sliding-Window Filtering with Constraints of Compactness and Recency in Incremental Database, In: Proceedings of the 4th International Conference on Networked Computing and Advanced Information Management, 2, 665–669, 2008 10.1109/NCM.2008.78Search in Google Scholar

[31] Srikant R., Agrawal R., Mining quantitative association rules in large relational tables, ACM SIGMOD Record, 25, 1996 10.1145/235968.233311Search in Google Scholar

[32] Srikant R., Agrawal R., Mining sequential patterns: Generalizations and performance improvements, In: Proceedings of the 5th International Conference on Extending Database Technology (EDBT’96), 3–17, 1996 10.1007/BFb0014140Search in Google Scholar

[33] Tiwari P., Shukla N., Multidimensional Sequential Pattern Mining, Int. J. Sci. Res. Publ., 2, 2012 Search in Google Scholar

[34] Zaki M.J., SPADE: An efficient algorithm for mining frequent sequences, Mach. Learn., 42, 31–60, 2001 in Google Scholar

Published Online: 2012-12-28
Published in Print: 2012-12-1

© 2012 Versita Warsaw

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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