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A Survey on Swarm and Evolutionary Algorithms for Web Mining Applications

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7077))

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Abstract

Internet is the biggest source of data and information today. It is the family of web sites and informative files. This paper focuses mainly on the web data and proposes some conceptual theories to extract knowledge through different web mining techniques like Clustering,FIS,ANN,LGP etc. We also focused on various aspects of applications of web mining in E-commerce & Business Intelligence. Finally, we discussed Swarm Intelligence(SI) techniques which are based on distributive self organized system such as Ant Colony Optimization (ACO), Stochastic Diffusion Search (SDS) and Particle Swarm Optimization (PSO) in brief in this survey which are preferred because of its vast uses and simplicity.

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References

  1. Abraham, A.: i-Miner, a Web Usage mining framework using Hierarchical Intelligent Systems. In: IEEE International Conference on Fuzzy Systems, FUZZY-IEEE 2003, pp. 1129–1134 (2003)

    Google Scholar 

  2. Abraham, A.: Business Intelligence from Web Usage Mining. Journal of Information & Knowledge Management 2(4), 4375–4390 (2003)

    Article  Google Scholar 

  3. Chi, E.H., Rosien, A., Heer, J.: Lumberjack: Intelligent Discovery and Analysis of Web User Traffic Composition. In: Proceedings of ACM SIGKDD Workshop on Web Mining for Usage Patterns and User Profiles. ACM Press, Canada (2002)

    Google Scholar 

  4. Kosala, R., Blockeel, H.: Web Mining research: A Survey. ACM SIGKDD Explorations 2(1), 1–15 (2002)

    Article  Google Scholar 

  5. Etzioni, O.: The World Wide Web: Quagmire or Gold Mine? Comm. ACM 39(11), 65–68 (1996)

    Article  Google Scholar 

  6. Srivastava, J., Desikan, P., Kumar, V.: Web Mining: Accomplishments and Future Directions. In: Proc. US Nat’l Science Foundation Workshop on Next-Generation Data Mining (NGDM), Nat’l Science Foundation (2002)

    Google Scholar 

  7. Chakrabarti, S., et al.: Mining Web’s Link Structure. Computer 32(8), 60–67 (1999)

    Article  Google Scholar 

  8. Kumar, R., et al.: Trawling the Web for Emerging Cyber communities. In: Proc. 8th World Wide Web Conf. Elsevier Science (1999)

    Google Scholar 

  9. Pitkow, J.E., Bharat, K.: WebViz: A Tool for WWW Access Log Analysis. In: Proc. 1st Int’l Conf. World Wide Web, pp. 271–277. Elsevier Science (1994)

    Google Scholar 

  10. Srivastava, J., et al.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. ACM SIGKDD Explorations 1(2), 12–23 (2000)

    Article  Google Scholar 

  11. Punin, J., Krishnamoorthy, M.: Extensible Graph Markup & Modeling Language Specification (1999), http://www.cs.rpi.edu/_puninj/XGMML/draftxgmml.html

  12. Punin, J., Krishnamoorthy, M.: Log Markup Language (LOGML) Specification (2000), http://www.cs.rpi.edu/_puninj/LOGML/draft-logml.html

  13. Maler, E., De Rose, S.: XML Linking Language (1998), http://www.w3.org/TR/WD-xlink

  14. Mannila, H., Toivonen, H., Verkamo, I.: Discovering frequent episodes in sequences. In: 1st Intl. Conf. Knowledge Discovery and Data Mining (1995)

    Google Scholar 

  15. Advances in Web Usage Mining and User Profiling. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836. Springer, Heidelberg (July 2000)

    Google Scholar 

  16. Mahat, P.: S I & Machine Learning. Res. Report, Dept. CS, LAMAR Univ.

    Google Scholar 

  17. Ansari, S., et al.: Integrating E-Commerce & data mining: Architecture & Challenges. In: WEBKDD 2000 Workshop (2000)

    Google Scholar 

  18. http://www.wikipedia.org

  19. http://en.wikipedia.org/wiki/Swarm_intelligence

  20. http://en.wikipedia.org/wiki/Ant_colony_optimization

  21. http://www.codeproject.com/cpp/GeneticandAntAlgorithms.asp

  22. http://www.aco-metaheuristic.org/

  23. http://en.wikipedia.org/wiki/Stochastic_Diffusion_Search

  24. http://en.wikipedia.org/wiki/Particle_swarm_optimization

  25. Grosan, C., et al.: Swarm Intelligence in Data Mining. SCI 34 I-20-2006. Springer, Heidelberg (2006)

    Book  MATH  Google Scholar 

  26. Chen, Y., Peng, L., Abraham, A.: Programming Hierarchical Takagi Sugeno Fuzzy Systems. In: 2nd International Symposium on Evolving Fuzzy Systems (EFS 2006). IEEE Press (2006)

    Google Scholar 

  27. Eberhart, R.C., Shi, Y.: Particle swarm optimization:developments,applications & resources. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, Seoul (2001)

    Google Scholar 

  28. Hu, X., Shi, Y., Eberhart, R.C.: Recent Advances in Particle Swarm. In: Proceedings of Congress on evolutionary Computation (CEC), Portland, Oregon, pp. 90–97 (2004)

    Google Scholar 

  29. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  30. Merkl, D.: Text mining with self-organizing maps. In: Handbook of Data Mining and Knowledge, pp. 903–910. Oxford University Press, Inc., New York (2002)

    Google Scholar 

  31. Pomeroy, P.: An Introduction to Particle Swarm Optimization (2003), http://www.adaptiveview.com/articles/ipsop1.html

  32. Settles, M., Rylander, B.: Neural network learning using particle swarm optimizers. In: Advances in Information Science and Soft Computing, pp. 224–226 (2002)

    Google Scholar 

  33. Sousa, T., Neves, A., Silva, A.: Swarm Optimisation as a New Tool for Data Mining. In: International Parallel and Distributed Processing Symposium (IPDPS 2003), p. 144b (2003)

    Google Scholar 

  34. Steinbach, M., Karypis, G., Kumar, V.: A Comparison of Document Clustering Techniques. In: Text Mining Workshop, KDD (2000)

    Google Scholar 

  35. Ujjin, S., Bentley, P.J.: Particle swarm optimization recommender system. In: Proceedings of the IEEE Swarm Intelligence Symposium (SIS 2003), Indianapolis, Indiana, USA, pp. 124–131 (2003)

    Google Scholar 

  36. Weng, S.S., Liu, Y.H.: Mining time series data for segmentation by using Ant Colony Optimization. European Journal of Operational Research (2006), http://dx.doi.org/10.1016/j.ejor.2005.09.001

  37. Dorigo, M., Bonaneau, E., Theraulaz, G.: Ant algorithms and stigmergy. Future Generation Computer Systems 16, 851–871 (2000)

    Article  Google Scholar 

  38. Abraham, A., Ramos, V.: Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming. In: IEEE Congress on Evolutionary Computation (CEC 2003), pp. 1384–1391. IEEE Press, Australia (2003) ISBN 0780378040

    Google Scholar 

  39. Thangavel, K., Jaganathan, P.: Rule Mining Algorithm with a New Ant Colony Optimization Algorithm. In: Proc. of the International Conference on Computational Intelligence & Multimedia Applications, December 3-15, vol. 2, pp. 135–140 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CSE & IT, MITS, Rayagada, Orissa, India

    Ashok Kumar Panda & Anirban Mitra

  2. Department of Inf. & Comm. Technology, F.M. University, Balesore, Orissa, India

    S. N. Dehuri

  3. Department of Computer Science, Berhampur University, Orissa, India

    M. R. Patra

Authors
  1. Ashok Kumar Panda
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  2. S. N. Dehuri
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  3. M. R. Patra
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  4. Anirban Mitra
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Department of Electronics and Telecommunications, Jadavpur University, 700032, Kolkata, India

    Swagatam Das

  4. ANITS, Visakhapatnam, India

    Suresh Chandra Satapathy

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© 2011 Springer-Verlag Berlin Heidelberg

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Panda, A.K., Dehuri, S.N., Patra, M.R., Mitra, A. (2011). A Survey on Swarm and Evolutionary Algorithms for Web Mining Applications. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27242-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-27242-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27241-7

  • Online ISBN: 978-3-642-27242-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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