Open Access   Article

Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach

A. Saravanan1 , S. Sathya Bama2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 21-26, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.2126

Online published on Feb 28, 2019

Copyright © A. Saravanan, S. Sathya Bama . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: A. Saravanan, S. Sathya Bama, “Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.21-26, 2019.

MLA Style Citation: A. Saravanan, S. Sathya Bama "Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach." International Journal of Computer Sciences and Engineering 7.2 (2019): 21-26.

APA Style Citation: A. Saravanan, S. Sathya Bama, (2019). Frequent Sequential Pattern Mining in Web Log Data – A Simple Approach. International Journal of Computer Sciences and Engineering, 7(2), 21-26.

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Abstract

With nurturing reputation of the World Wide Web, large quantity of web usage data is collected by the web servers and stored in web access log files. Web usage mining is a technology to mine valuable knowledge from the World Wide Web. It intends to discover interesting user access patterns from web log files. Analysis of these user access patterns is used to determine that the information architecture of the web site can be reorganized to better facilitate information retrieval. Association rule mining is also used to find association relationships amongst large data sets. Mining frequent sequential patterns is a significant aspect in association rule mining. Based on this, the changes can be suggested to the web site by placing embedded hyperlinks on the home page to the frequently accessed sections of the web site. In this paper, a very efficient algorithm has been adopted for expert systems to mine frequent sequential patterns in web usage data

Key-Words / Index Term

Data Mining, Web Mining, Association rule mining, Frequent sequential pattern mining, Web Log files, Web usage

References

[1] Renáta Iváncsy, István Vajk, “Frequent Pattern Mining in Web Log Data”, Acta Polytechnica Hungarica, Vol. 3, No. 1, 2006.
[2] S. K. Madria, S. S. Bhowmick, W. K. Ng, and E.-P. Lim, “Research issues in web data mining”, Data Warehousing and Knowledge Discovery, pp. 303-312, 1999.
[3] J. Borges and M. Levene, “Data mining of user navigation patterns, WEBKDD, pp. 92-111, 1999.
[4] M. N. Garofalakis, R. Rastogi, S. Seshadri, and K. Shim, “Data mining and the web: Past, present and future”, ACM CIKM’99 2nd Workshop on Web Information and Data Management (WIDM’99), Kansas City, Missouri, USA, C. Shahabi, Ed. ACM, pp. 43-47, 1999.
[5] S. Chakrabarti, “Data mining for hypertext: A tutorial survey”, SIGKDD: SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery and Data Mining, ACM, Vol. 1, No. 2, pp. 1-11, 2000.
[6] M. Eirinaki and M. Vazirgiannis, “Web mining for web personalization”, ACM Trans. Inter. Tech., Vol. 3, No. 1, pp. 1-27, 2003.
[7] J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, “Mining access patterns efficiently from web logs”, PADKK ’00: Proceedings of the 4th Pacific- Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications. London, UK: Springer-Verlag, pp. 396-407, 2000.
[8] Vinita Shrivastava, Neetesh Gupta, “Performance Improvement Of Web Usage Mining By Using Learning Based K-Mean Clustering”, International Journal of Computer Science and its Applications.
[9] Craig Peter Oosthuizen, “Web Usage Mining of Organisational Web Sites”, December 2005.
[10] B.Mortazavi-asl, “Discovering and Mining User Web-Page Traversal Patterns”, Computer Science, Simon Fraser University, 1999.
[11] M. Géry and H.Haddad, “Evaluation of web usage mining approaches for user`s next request prediction”, Proc. 5th ACM International workshop on web information and data management, New Orleans, Louisiana, USA. pp 74-81. 7-8 November, 2003.
[12] R. Kosala, H. Blockeel, “Web Mining Research: A Survey”, SIGKKD Explorations, vol. 2(1), July 2000.
[13] J. Srivastava, R. Cooley, M. Deshpande, P.-N. Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data”, SIGKKD Explorations, vol.1, Jan 2000.
[14] J. Punin, M. Krishnamoorthy and M. Zaki, “Web usage mining: Languages and algorithms, Studies in Classification”, Data Analysis, and Knowledge Organization. Springer-Verlag, 2001.
[15] M. S. Chen, J. S. Park, and P. S. Yu, “Data mining for path traversal patterns in a web environment”, Sixteenth International Conference on Distributed Computing Systems, pp. 385-392, 1996.
[16] P. Batista, M. Ario, and J. Silva, “Mining web access logs of an on-line newspaper”, 2002.
[17] O. R. Zaiane, M. Xin, and J. Han, “Discovering web access patterns and trends by applying olap and data mining technology on web logs”, ADL ’98: Proceedings of the Advances in Digital Libraries Conference. Washington, DC, USA: IEEE Computer Society, pp. 1-19, 1998.
[18] J. F. F. M. V. M. Li Shen, Ling Cheng and T. Steinberg, “Mining the most interesting web access associations”, WebNet 2000-World Conference on the WWW and Internet, 2000, pp. 489-494.
[19] B. Jeudy and F. Rioult, “Database transposition for constrained closed pattern mining”, Proceedings of Third International Workshop on Knowledge Discovery in Inductive Databases (KDID) co-located with ECML/PKDD, 2004.
[20] R. Agrawal, R. Srikant, “Fast algorithms for mining association rules”, Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487–499.
[21] J. Han, “Research challenges for data mining in science and engineering”, NGDM 2007.
[22] R. Agrawal, R. Srikant, “Mining sequential patterns”, Proceedings of the 11th International Conference on Data Engineering, pp. 3, 1995.
[23] Han J, Pei J, Yin Y, “Mining frequent patterns without candidate generation”, Proceeding of the 2000 ACM-SIGMOD international conference on management of data (SIGMOD’00), Dallas, TX, pp 1–12, 2000.
[24] Jian Pei, Jiawei Han et al, “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth”, Proceeding ICDE `01 Proceedings of the 17th International Conference on Data Engineering, IEEE Computer Society Washington, DC, USA, 2001.
[25] Jiawei Han, Hong Cheng, Dong Xin, “Frequent pattern mining: current status and future directions”, Data Mining and Knowledge Discovery, 15, 55–86, 2000.
[26] P.V. Nikam, D.S. Deshpande, “Different Approaches for Frequent Itemset Mining”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.10-14, 2018.
[27] Pradeep Chouksey, “Mining Frequent model Using mass-produced Approach”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.89-94, 2017.
[28] Sunil Joshi, “A Dynamic Approach for Frequent Pattern Mining Using Transposition of Database”, The IEEE 2010 International Conference on Communication software and Netweorks (ICCSN 2010) from 26 - 28 February 2010.
[29] Sunil Joshi, Dr. R. S . Jadon, Dr. R. C. Jain, “An Implementation of Frequent Pattern Mining Algorithm using Dynamic Function”, International Journal of Computer Applications (0975 – 8887), Volume 9– No.9, November 2010.
[30] Sonia Sharma, Munishwar Rai, “Customer Behaviour Analysis using Web Usage Mining”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.47-50, 2017.