The week's pick
Random Articles
Reseach Article
An Approach Proposed for Detecting Users activities from Recorded Log
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 135 - Number 13 |
| Year of Publication: 2016 |
| Authors: Deepti Sahu, Rishi Soni |
10.5120/ijca2016908545
|
Deepti Sahu, Rishi Soni . An Approach Proposed for Detecting Users activities from Recorded Log. International Journal of Computer Applications. 135, 13 ( February 2016), 29-35. DOI=10.5120/ijca2016908545
Abstract
The development of the web has created a big challenge for directing the client to the website pages in their area of interest. Accordingly, just option is to capture the intuition of the client and provide them a list of recommendation. Most specifically, online navigation activities develop with day by day; consequently extract information with the capability of intelligence, from these activities is a tedious job. Webmaster of an organization ought to utilize methods of web mining to fetch intuition, Web usage mining (WUM) is one among them.WUM is designed to operate on web server logs; logs contain client's navigation history which is very useful for the web recommendation. Recommendation is an application of web usage mining. Consequently, recommendation system can be utilized to forecast the navigation pattern of client and recommend those to client in a form of recommendation list. This paper, suggest a recommendation principal that recommends a list of pages on the basis of client's past navigation history (recorded within the web log). This approach brings the advance within the precision of displayed pages for the client or users.
References
- M. Kantardzic, “Data Mining: Concepts, Models, Methods, and Algorithms”, John Wiley & Sons Inc., New York, 2002.
- R. Agrawal and R. Srikant, “Mining Sequential Patterns”, In Proceedings of the 11th International Conference on Data Engineering, pp. 3-14, Taipei, Taiwan, 1995.
- M. Lie and L. Fan, “A Web Personalization System Based on Users’Interested Domains,”Proc. 7th IEEE Int. Conf.on Cognitive Informatics (ICCI'08)-IEEE 2008, pp.153-159.
- ThabetSlimani, and Amor Lazzez, “Sequential Mining: Patterns And Algorithms Analysis”, Computer Science, Taif University & LARODEC Lab, Saudia Arabia, 2 Computer Science,Taif University , Saudia Arabia.
- Zhou. B,Hui. S.C and Chang.K, "An intelligent recommender system using sequential Web access patterns", IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 393 - 398, December 2004.
- Cui Wei, Wu Sen, Zhang Yuan and Chen Lian-Chang, "Algorithm of mining sequential patterns for web personalization services", ACM SIGMIS Database, vol. 40 , no. 2, pp. 57-66,May 2009.
- Zhenglu Yang, Yitong Wang and Masaru Kitsuregawa, "An Effective System for Mining Web Log", LNCS, vol. 3841, pp.40-52, 2006.
- Mobasher, H. Dai, T. Luo and M. Nakagawa, “Effective personalization based on association rule discovery from web usage.
- SizuHou, Xianfei Zhang, "Alarms Association Rules Based on Sequential Pattern Mining Algorithm," In proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 2, pp.556-560, Shandong, 2008.
- R. Agrawal and R. Srikant, “Mining Sequential Patterns”, In Proceedings of the 11th International Conference on Data Engineering, pp. 3-14, Taipei, Taiwan, 1995.
- Zhenglu Yang, Yitong Wang and MasaruKitsuregawa, "An Effective System for Mining Web Log", LNCS, vol. 3841, pp.40-52, 2006.
- Shengnan Cong, Jiawei Han and David Padua, "Parallel Mining Of Closed Sequential Patterns", in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 562 – 567, Chicago, Illinois, USA, 2005.
- Yan. X, Han. J and Afshar. R, "CloSpan: mining closed sequential patterns in large datasets", In Proceedings of the 3rd SIAM International Conference on data mining, pp. 166-177, San Francisco, CA, May 2003.
- Ming-Yen Lin, Sue-Chen Hsueh and Chia-Wen Chang, "Mining Closed Sequential Patterns with Time Constraints", Journal of Information Science and Engineering, vol. 24, pp. 33-46, 2008.
- MagdaliniEirinaki and MichalisVazirgiannis, "Web Mining for Web Personalization", ACM Transactions on Internet Technology (TOIT), vol. 3, no.1, pp. 1 – 27 , 2003.
- J. Ben Schafer, Joseph A. Konstan and John T. Riedl, "Recommender Systems for the Web", In Visualizing the Semantic Web, Springer, pp.102-123, 2006.
- OlfaNasraoui, Zhiyong Zhang, and EsinSaka, "Web Recommender System Implementations in Multiple Flavors: Fast and (Care) Free for All", in Proceedings of SIGIR Open Source Information Retrieval Worskhop, Seattle, WA, July 2006.
- J. Schafer, J. Konstan, and J. Riedl, “Recommender Systems in E-Commerce”, in Proceedings of ACM Conference on Electronic Commerce (EC-99), pp.158-166, 1999.
- Hiroshi Ishikawa, Manabu Ohta, Shohei Yokoyama, Junya Nakayama, and Kaoru Katayama, "On the Effectiveness of Web Usage Mining for Page Recommendation and Restructuring", Lecture Notes in Computer Science, Springer Berlin / Heidelberg, vol. 2593, pp. 253-267, 2009.
- Osmar R. Zaiane, Jia Li, and Robert Hayward, "Mission-Based Navigational Behaviour Modeling for Web Recommender Systems", in proceedings of the 6th International workshop on knowledge discovery, Seattle, WA, USA, August 22-25, 2004.
- Mehdi Adda, PetkoValtchev, RokiaMissaoui and ChabaneDjeraba, "Toward Recommendation Based on Ontology Powered Web-Usage Mining", Internet Computing, vol. 11, no.4, pp. 45-52, 2007.
- Sarabjot Singh Anand and BamshadMobasher, "Intelligent Techniques for Web Personalization", Lecture Notes in Computer Science, Vol. 3169, Springer, 2005.
- Utpala Niranjan1, Dr.R.B.V. Subramanyam2, Dr.V.Khanaa, “An Efficient System Based On Closed Sequential Patterns for Web Recommendations”, IJCSI International Journal of Computer S cience Issues, Vol. 7, Issue 3, No 4, May 2010.
Index Terms
Keywords