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Mining Optimal Utility Incorporated Sequential Pattern from RFID Data Warehouse Using Genetic Algorithm

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Software Engineering and Computer Systems (ICSECS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 181))

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Abstract

Today, identification of sequential patterns from a huge database sequence is a major problem in the field of KDD. In addition, if the entire set of sequential patterns existing in a large database is presented, the user may find it difficult to understand and employ the mined result. In order to overcome these issues, we propose an efficient data mining system to generate the most favorable sequential patterns. The proposed technique first generates datasets from the warehoused RFID data. Each mined pattern has distinct utility and the most favorable sequential patterns are generated from the mined sequential patterns by using Genetic Algorithm (GA). A fitness function is used in GA to find out the sequential pattern that provides maximum profit. The implementation result shows that the proposed mining system accurately extracts the important RFID tags and its combinations, nature of movement of the tags and the optimum sequential patterns.

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References

  1. Li, B., Shasha, D.: Free Parallel Data Mining. ACM SIGMOD Record 27(2), 541–543 (1998)

    Article  Google Scholar 

  2. Anand, S.S., Bell, D.A., Hughes, J.G.: EDM: A general framework for data mining based on evidence theory. Data and Knowledge Engineering 18(3), 189–223 (1996)

    Article  MATH  Google Scholar 

  3. Agrawal, R., Imielinsk, T., Swami, A.: Database Mining: A Performance Perspective. IEEE Transaction Knowledge and Data Engineering 5(6), 914–925 (1993)

    Article  Google Scholar 

  4. Chen, S.Y., Liu, X.: Data mining from 1994 to 2004: an application-oriented review. International Journal of Business Intelligence and Data Mining 1(1), 4–11 (2005)

    Article  Google Scholar 

  5. Singh, D.R.Y., Chauhan, A.S.: Neural Networks In Data Mining. Journal of Theoretical and Applied Information Technology 5(6), 36–42 (2009)

    Google Scholar 

  6. Roberts, C.M.: Radio frequency identification (RFID). Computers & Security 25, 18–26 (2006)

    Article  Google Scholar 

  7. Aboalsamh, H.A.: A novel Boolean algebraic framework for association and pattern mining. WSEAS Transactions on Computers 7(8), 1352–1361 (2008)

    Google Scholar 

  8. Sathiyamoorthi, V., Bhaskaran, V.M.: Data Mining for Intelligent Enterprise Resource Planning System. International Journal of Recent Trends in Engineering 2(3), 1–5 (2009)

    Google Scholar 

  9. Ranjan, J., Bhatnagar, V.: A Review of Data Mining Tools In Customer Relationship Management. Journal of Knowledge Management Practice 9(1) (2008)

    Google Scholar 

  10. Shaw, M.J., Subramaniam, C.S., Tan, G.W., Welge, M.E.: Knowledge management and data mining for marketing. Decision support systems 31(1), 127–137 (2001)

    Article  Google Scholar 

  11. Sabbaghi, A., Vaidyanathan, G.: Effectiveness and Efficiency of RFID technology in Supply Chain Management: Strategic values and Challenges. Journal of Theoretical and Applied Electronic Commerce Research 3(2), 71–81 (2008) ISSN 0718–1876

    Article  Google Scholar 

  12. Asif, Z., Mandviwalla, M.: Integrating the supply chain with RFID: a technical and business analysis. Communications of the Association for Information Systems 15(24), 393–427 (2005)

    Google Scholar 

  13. Pei, J., Han, J., Asl, B.M., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsum, M.C.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering 16(10), 1–17 (2004)

    Article  Google Scholar 

  14. Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8(6), 866–883 (1996)

    Article  Google Scholar 

  15. Chen, Y.L., Hu, Y.H.: Constraint-based sequential pattern mining: The consideration of recency and compactness. Decision Support Systems 42, 1203–1215 (2006)

    Article  Google Scholar 

  16. Jea, K.F., Lin, K.C., Liao, I.E.: Mining hybrid sequential patterns by hierarchical mining technique. International Journal of Innovative Computing, Information and Control 5(8) (2009)

    Google Scholar 

  17. Saputra, D., Rambli, D.R.A., Foong, O.M.: Mining Sequential Patterns Using I-PrefixSpan. International Journal of Computer Science and Engineering 2(2), 49–554 (2008)

    Google Scholar 

  18. Hu, J., Mojsilovic, A.: High-utility pattern mining: A method for discovery of high-utility item sets. Pattern Recognition 40, 3317–3324 (2007)

    Article  MATH  Google Scholar 

  19. Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: The pattern-growth methods. Journal of Intelligent Information Systems 28(2), 133–160 (2007)

    Article  Google Scholar 

  20. Sakurai, S., Kitahara, Y., Orihara, R.: A Sequential Pattern Mining Method based on Sequential Interestingness. International Journal of Computational Intelligence 4(4), 252–260 (2008)

    Google Scholar 

  21. Exarchos, T.P., Tsipouras, M.G., Papaloukas, C., Fotiadis, D.I.: A two-stage methodology for sequence classification based on sequential pattern mining and optimization. Data & Knowledge Engineering 66, 467–487 (2008)

    Article  Google Scholar 

  22. Shankar, S., Purusothaman, T.: Utility Sentient Frequent Itemset Mining and Association Rule Mining: A Literature Survey and Comparative Study. International Journal of Soft Computing Applications 10(4), 81–95 (2009)

    Google Scholar 

  23. Ykhlef, M., ElGibreen, H.: Mining Sequential Patterns Using Hybrid Evolutionary Algorithm. World Academy of Science, Engineering and Technology 60, 863–870 (2009)

    Google Scholar 

  24. Pillai, J., Vyas, O.P.: Overview of Itemset Utility Mining and its Applications. International Journal of Computer Applications 5(11), 9–13 (2010)

    Article  Google Scholar 

  25. Sedighizadeh, M., Rezazadeh, A.: Using Genetic Algorithm for Distributed Generation Allocation to Reduce Losses and Improve Voltage Profile. World Academy of Science, Engineering and Technology 37 (2008)

    Google Scholar 

  26. Radhakrishnan, P., Prasad, V.M., Gopalan, M.R.: Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management. Journal of Computer Science 5(3), 233–241 (2009)

    Article  Google Scholar 

  27. Al-Maqaleh, B.M., Bharadwaj, K.K.: Genetic Programming Approach to Hierarchical Production Rule Discovery. World Academy of Science, Engineering and Technology 11, 43–46 (2005)

    Google Scholar 

  28. Mantere, T.: A Min-Max Genetic Algorithm with Alternating Multiple Sorting for Solving Constrained Problems. In: Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (2006)

    Google Scholar 

  29. Diaz-Gomez, P.A., Hougen, D.F.: Improved Off-Line Intrusion Detection Using A Genetic Algorithm. In: Proceedings of the Seventh International Conference on Enterprise Information Systems, Miami, USA, May 25-28, pp. 66–73 (2005)

    Google Scholar 

  30. Reddy, S.R.: Selection of RTOS for an Efficient Design of Embedded Systems. International Journal of Computer Science and Network Security 6(6), 29–37 (2006)

    Google Scholar 

  31. Schenk, S., Hanke, K.: Combining Genetic Algorithms With Imperfect And Subdivided Features For The Automatic Registration Of Point Clouds (GAREG-ISF). In: Proceedings of the 3rd ISPRS International Workshop, vol. 38 (2009)

    Google Scholar 

  32. Korejo, I., Yang, S., Li, C.: A Comparative Study of Adaptive Mutation Operators for Genetic Algorithms. In: Proceedings of the 8th Metaheuristic International Conference, July 13-16 (2009)

    Google Scholar 

  33. Sewell, M., Samarabandu, J., Rodrigo, R., McIsaac, K.: The Rank-scaled Mutation Rate for Genetic Algorithms. International Journal of Information Technology 3(1) (2006)

    Google Scholar 

  34. Bankovic, Z., Moya, J.M., Araujo, A., Bojanic, S., Taladriz, O.N.: A Genetic Algorithm-based Solution for Intrusion Detection. Journal of Information Assurance and Security 4, 192–199 (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. H.O.D (IT), GNIM & Research Scholar, Department of Computer Science & Applications, M.D.U Rohtak, India

    Barjesh Kochar

  2. Department of Computer Science & Applications, M.D.U Rohtak, India

    Rajender Singh Chhillar

Authors
  1. Barjesh Kochar
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  2. Rajender Singh Chhillar
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Editor information

Editors and Affiliations

  1. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Gambang, Kuantan, Pahang, Malaysia

    Jasni Mohamad Zain  & Wan Maseri bt Wan Mohd  & 

  2. Information Systems Department, King Saud University, 11543, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

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© 2011 Springer-Verlag Berlin Heidelberg

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Kochar, B., Chhillar, R.S. (2011). Mining Optimal Utility Incorporated Sequential Pattern from RFID Data Warehouse Using Genetic Algorithm. In: Zain, J.M., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22203-0_56

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  • DOI: https://doi.org/10.1007/978-3-642-22203-0_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22202-3

  • Online ISBN: 978-3-642-22203-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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