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METHODOLOGIES FOR PATTERN DISCOVERY FROM THE WEB TRANSACTIONS
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Author(s):
OMKAR SINGH , AVNEESH ANAND SINGH , VERINDER KUMAR , JAYANT SHARMA
Vol - 2, Issue- 2 ,
Page(s) : 103 - 111
(2011 )
DOI : https://doi.org/10.32804/IRJMST
Abstract
The field of data mining and pattern discovery emerged in the recent past as a result of the dramatic evolution of the technology for information storage, access, and analysis. Data mining deals with the problem of discovering unknown patterns from data. Web mining is the integration of information gathered by traditional data mining methodologies and techniques with information gathered over the World Wide Web.
In this paper, we survey some of the recent approaches defined as the discovery and analysis of useful information from the World Wide Web. It helps in predicting future trends and behaviors, allowing businesses to make proactive and knowledge-driven decisions. Data mining finds patterns and relationships by using sophisticated techniques to build models abstract representations of reality. A good model is a useful guide to understanding your business and making decisions.
- R. Agrawal and R. Srikant. “Fast algorithms for mining association rules”. In Proceedings of the 20th VLDB Conference, Santiago, Chile, pp. 487-499, 1994.
- J. Borges and M. Levene. “Ranking pages by topology and popularity within web sites”, World Wide Web Journal, 9, pp.301-316, 2006.
- J. Borges and M. Levene. “Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions”, IEEE Transactions on Knowledge and Data Engineering, 19 (4), pp. 441-452, April 2007.
- J Barges e Mark Levene. “A Clustering-Based Approach for Modeling: User Navigation With Increased Accuracy”. In Proceedings of the Second International Workshop on knowledge Discovery from Data Streams (IWKDDS) in conjunction with PKDD, Porto, Portugal, Outubro, 2005.
- M. S. Chen, J. S. Park, and P. S. Yu. “Data mining for path traversal patterns in a web environment”. In Proceedings of the 16th International Conference on Distributed Computing Systems, pages 385-392, 1996.
- R. Cooley, B. Mobasher, and J. Srivastava. “Grouping web page references into transactions for mining world wide web browsing patterns”. Technical Report Tr. 97-021, University of Minnesota, Dept. of Computer Science, Minneapolis,1997.
- M. Eirinaki and M., Vazirgiannis. Web Mining for Web Personalization, Athens University of Economics and Business, 2003.
- J. Huysmans, B. Baesens, and J. Vanthienen. Web Usage Mining: A Practical Study, Katholieke Universiteit Leuven, Dept. of Applied Economic Sciences, 2003.
- L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
- I. Khosla, B. Kuhn, and N. Soparkar. “Database search using information mining”. In Proceedings of Int. Conf. on Management of Data, Montreal (ACM-SIGMOD), Quebec, 1996.
- T. Kirk, A. Y. Levy, Y. Sagiv, and D. Shrivastava. “The information manifold”. In Working Notes of the AAAI Spring Symposium, Information Gathering from Heterogeneous, Distributed Environments, AAAI Press, 1995.
- H. Mannila, H. Toivonen, and A. I. Verkamo. “Discovering frequent episodes in sequences. In Proceedings of the First Int’l Conference on Knowledge Discovery and Data Mining, pp. 210-215, Montreal, Quebec, 1995.
- Open Market Inc. Open market web reporter. http://www.openmarket.com.
- Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, “Effective Personalization Based on Association Rule Discovery from Web Usage Data”, WIDM01, 3rd ACM Workshop on Web Information and Data Management, November 9, 2001, Atlanta, Georgia, USA.
- R. Ng and J. Han. “Efficient and effective clustering method for spatial data mining”. In Proceedings of the 20th VLDB Conference, pages 144-155, Santiago, Chile, 1994.
- J. Pei, J. Han, B. Mortazavi-asl, and H. Zhu. “Mining Access Patterns Efficiently from Web Logs”. In Proceedings of the 4th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pages 396-407, April 2000.
- M. Perkowitz and O. Etzioni. “Adaptive web sites: Automatically synthesizing web pages”. In Proceedings of the Fifteenth National Conf. on Artificial Intelligence (AAAI), pp. 727-732, 1998.
- P. Pirolli, J. Pitkow, and R. Rao. “Silk from a sow’s ear: Extracting usable structures from the web”. In Proceedings of 1996 Conference on Human Factors in Computing Systems (CHI-96), Vancouver, British Columbia, Canada, 1996.
- J. Pitkow. In search of reliable usage data on the www. In Sixth International World Wide Web Conference, pages 451-463, Santa Clara, CA, 1997.
- A. Savasere, E. Omiecinski, and S. Navathe. “An efficient algorithm for mining association rules in large databases”. In Proeedings of the 21th VLDB Conference, pages 432-443, Aurich, Switzerland, 1995.
- Software Inc. Webtrends. http://www.webtrends.com, 1995.
- R. Srikant and R. Agrawal. “Mining sequential patterns: Generalizations and performance improvements”. In Proceedings of the Fifth Int’l Conference on Extending Database Technology, Avignon, France, 1996.
- R. Srikant and Y. Yang. “Mining Web Logs to Improve Website Organization”. In Proceedings of the 10th international conference on World Wide Web , Hong Kong, pp. 430 – 437, 2001.
- Weinan Yang and Osmar Zaiane, “Clustering Web sessions by sequence alignment”, In Proceedings of the third International Workshop on Management of Information on the Web in Conjunction with 13th International Conference on Database and Expert Systems Applications, Conference on Database and Expert Systems Applications, pp. 394-398, 2002.
- O. R. Zaane and J. Han. “Resource and knowledge discovery in global information systems: A preliminary design and experiment”. In Proceedings of the First Int’l Conference on Knowledge Discovery and Data Mining, pages 331-336, Montreal, Quebec, 1995.
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