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Extracting Sequential Patterns Based on User Defined Criteria

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Hybrid Artificial Intelligent Systems (HAIS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8073))

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Abstract

Sequential pattern extraction is essential in many applications like bioinformatics and consumer behavior analysis. Various frequent sequential pattern mining algorithms have been developed that mine the set of frequent subsequences satisfying a minimum support constraint in a transaction database. In this paper, a hybrid framework to sequential pattern mining problem is proposed which combines clustering together with a novel pattern extraction algorithm that is based on an evaluation function, which utilizes user-defined criteria to select patterns. The proposed solution is applied on Web log data and Web domain, however, it can work in any other domain that involves sequential data as well. Through experimental evaluation on two different datasets, we show that the proposed framework can achieve valuable results for extracting patterns under user defined selection criteria.

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References

  1. Leung, C.W., Chan, S.C., Chung, F.: A Collaborative Filtering Framework Based on Fuzzy Association Rules and Multiple-Level Similarity. Knowl. Inf. Syst. 10(3), 357–381 (2006)

    Article  Google Scholar 

  2. Senkul, P., Salin, S.: Improving Pattern Quality in Web Usage Mining by Using Semantic Information. Knowl. Inf. Syst. 30(3), 527–541 (2011)

    Article  Google Scholar 

  3. Shyu, M., Haruechaiyasak, C., Chen, S.: Mining User Access Patterns with Traversal Constraint for Predicting Web Page Requests. Knowl. Inf. Syst. 10(4), 515–528 (2006)

    Article  Google Scholar 

  4. Bonnin, G., Brun, A., Boyer, A.: A Low-Order Markov Model Integrating Long-Distance Histories for Collaborative Recommender Systems. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, Florida, USA, pp. 57–66 (2009)

    Google Scholar 

  5. Mooney, C.H., Roddick, J.F.: Sequential Pattern Mining – Approaches and Algorithms. ACM Computing Surveys (CSUR) Surveys Homepage Archive 45(2) (2013)

    Google Scholar 

  6. Kirmemis Alkan, O., Karagoz, P.: Assisting Web Site Navigation Through Web Usage Patterns. In: IEA/AIE 2013, Amsterndam, Netherlands (June 2013)

    Google Scholar 

  7. Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  8. Ren, J.D., Cheng, Y.B., Yang, L.L.: An Algorithm for Mining Generalized Sequential Patterns. In: Proceedings of International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1288–1292 (2004)

    Google Scholar 

  9. Banerjee, A., Ghosh, J.: Clickstream Clustering Using Weighted Longest Common Subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining (2001)

    Google Scholar 

  10. Daoud, M., Lechani, L., Boughanem, M.: Towards a Graph-Based User Profile Modeling for a Session Based Personalized Search. Knowl. Inf. Syst. 21(3), 365–398 (2009)

    Article  Google Scholar 

  11. Kothari, R., Mittal, P.A., Jain, V., Mohania, M.K.: On Using Page Cooccurrences for Computing Clickstream Similarity. In: Proceedings of the 3rd SIAM International Conference on Data Mining, San Francisco, USA (2003)

    Google Scholar 

  12. Eiron, N., McCurley, K.S.: Untangling Compound Documents on the Web. In: Proceedings of ACM Hypertext, pp. 85–94 (2003)

    Google Scholar 

  13. Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.: Self-Organization and Identification of Web Communities. IEEE Computer 35(3) (2002)

    Google Scholar 

  14. Greco, G., Greco, S., Zumpano, E.: Web Communities: Models and Algorithms. World Wide Web 7(1), 58–82 (2004)

    Article  Google Scholar 

  15. Xie, Y., Phoha, V.V.: Web User Clustering from Access Log Using Belief Function. In: Proceedings of the First International Conference on Knowledge Capture, pp. 202–208. ACM Press (2001)

    Google Scholar 

  16. Pinto, H., Han, J., Pei, J., Wang, K.: Multi-dimensional Sequence Pattern Mining. In: CIKM (2001)

    Google Scholar 

  17. Bezerra, B.L.D., Carvalho, F.A.T.: Symbolic Data Analysis Tools for Recommendation Systems. Knowl. Inf. Syst. 21(3), 385–418 (2010)

    Google Scholar 

  18. Nasraoui, O., Gonzalez, F., Dasgupta, D.: The Fuzzy Artificial Immune System: Motivations, Basic Concepts, and Application to Clustering and Web Profiling. In: Proceedings of the World Congress on Computational Intelligence (WCCI) and IEEE International Conference on Fuzzy Systems, pp. 711–716 (2002)

    Google Scholar 

  19. Mobasher, B., Dai, H., Tao, M.: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)

    Article  MathSciNet  Google Scholar 

  20. Gunduz, S., Ozsu, M.T.: A Web Page Prediction Model Based on Clickstream Tree Representation of User Behavior. In: SIGKDD 2003, USA, pp. 535–540 (2003)

    Google Scholar 

  21. Patil, S.S.: A Least Square Approach to Analyze Usage Data for Effective Web Personalization. In: Proceedings of International Conference on Advances in Computer Science, pp. 110–114 (2011)

    Google Scholar 

  22. Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: International Conference on Tools with Artificial Intelligence, Newport Beach, pp. 558–567. IEEE (1997)

    Google Scholar 

  23. Yang, Q., Fan, J., Wang, J., Zhou, L.: Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model. In: Proceedings of the 10th International Conference on Data Mining, ICDM, Sydney, pp. 1145–1150 (2010)

    Google Scholar 

  24. Pei, J., Han, J., Mortazavi-Asi, B., Pino, H.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix- Projected Pattern Growth. In: ICDE 2001 (2001)

    Google Scholar 

  25. Mobasher, B.: Data Mining for Web Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 90–135. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  26. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGDD Explorations 1(2), 12–23 (2000)

    Article  Google Scholar 

  27. Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowl. Inf. Syst. 1(1), 12–23 (1999)

    Article  Google Scholar 

  28. Etzioni, O.: The World Wide Web: Quagmire or gold mine? Communications of the ACM 39(11), 65–68 (1996)

    Article  Google Scholar 

  29. Han, J., Pei, J., Mortazavi-Asi, B., Chen, Q., Dayal, U., Hsu, M.C.: FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining. In: SIGKDD 2000 (2000)

    Google Scholar 

  30. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE (1995)

    Google Scholar 

  31. Fu, Y., Sandhu, K., Shih, M.Y.: Clustering of Web Users Based on Access Patterns. In: Proceedings of WEBKDD (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Middle East Technical University (METU), 06800, Ankara, Turkey

    Oznur Kirmemis Alkan & Pınar Karagoz

Authors
  1. Oznur Kirmemis Alkan
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  2. Pınar Karagoz
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios M. Polycarpou

  3. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  4. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  5. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Héctor Quintián  & Emilio Corchado  & 

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Alkan, O.K., Karagoz, P. (2013). Extracting Sequential Patterns Based on User Defined Criteria. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_19

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  • DOI: https://doi.org/10.1007/978-3-642-40846-5_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40845-8

  • Online ISBN: 978-3-642-40846-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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