An Efficient Maximal Web Access Patterns Mining
Web use mining is a fascinating application of information mining which gives understanding into client conduct on the Internet. An imperative system to find client access and route trails is focused around consecutive examples mining. One of the key difficulties for web access examples mining is handling the issue of mining lavishly organized examples. To recognize and anticipate more mind boggling website page demands. Relating CAP mining and demonstrating strategies are proposed and demonstrated to be successful in the quest for and representation of concurrency between access designs on the web. (To begin with Occurrence List Mine)  From analyses led on vast scale manufactured grouping information and in addition genuine web access information. The web usage data provides the paths leading to accessed Web pages with preferences and higher priorities. This information is often gathered automatically into access logs through the Web server. Accessing Information is the most frequent task. It is a top-down method that uses the concept of first occurrence to reduce search space and thus improving the Performance.
Mining maximal web access patterns- a new approach by international journal of machine intelligence issn: 0975–2927 & e-issn: 0975–9166, volume 3, issue 4, 2011.
Rajimol A. and Raju G.(2011) FOL-Mine – International conference on Advances in Computing and Communication (ACC2011),253- 262
Agrawal R. and Srikant R. (1994) In: 20th International Conference on Very Large Databases, Santiago, Chile, 487–499.
Pei J., Han J., Mortazavi-asl B. and Zhu, H. (2000) 4th PAKDD, Kyoto, Japan, 396-407.
Bayardo R. J. (1998) ACM-SIGMOD International Conference on Management of Data, 85-93.
Burdick D., Calimlim M., and Gehrke J. (2001) International Conference on Data Engineering.
Gouda K. and Zaki M.J. (2010) International Conference on Knowledge Discovery and Data Mining.
CHENG L.V., WEI Chu-yuan and ZHANG Hantao. (2006) Journal of Communication and Computer, 3(11)
UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]
B. Özden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. In Proc. 1998 Int. Conf. Data Engineering (ICDE’98), pages 412–421, Orlando, FL, Feb. 1998.
M. Perkowitz and O. Etzioni. Adaptive sites: Automatically learning from user access patterns. In Proc. 6th Int’l World Wide Web Conf., Santa Clara, California, April 1997.
M. Spiliopoulou and L. Faulstich. WUM: A tool for Web utilization analysis. In Proc. 6th Int’l Conf. on Extending Database Technology (EDBT’98), Valencia, Spain, March 1998.
T. Sullivan. Reading reader reaction: A proposal for inferential analysis of Web server log files. In Proc. 3rd Conf. Human Factors & The Web, Denver, Colorado, June 1997.
Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. In 3rd Intl. Conf. on Knowledge Discovery and Data Mining.
Zaki, M. J., Lesh, N., & Ogihara, M. (1998). PLANMINE: Sequence mining for plan failures. In 4th Intl. Conf. Knowledge Discovery and Data Mining.
Zaki, M. J. (1998). Efficient enumeration of frequent sequences. In 7th Intl. Conf. on Information and Knowledge Management.
R. Agrawal, R. Srikant. Fast algorithms for mining association rules. In: Proc. of the 20th VLDB Conference, Santiago, Chile, 1994, pp.487-499.
Bray, J. Paoli, C. M. Sperberg-McQueen. Extensible markup language (XML) 1.0 W3C recommendation. Technical report, W3C, 1998.
M. Balabanovic, Y. Shoham. Learning information retrieval agents: Experiments with automated Web browsing. In: On-line Working Notes of the AAAI Spring Symposium Series on Information Gathering from Distributed, Heterogeneous Environments, 1995.
R. Cooley, B. Mobasher, J. Srivastava. Web mining: Information and pattern discovery on the World Wide Web. In: International Conference on Tools with Artificial Intelligence, Newport Beach, CA, 1997, pp. 558-567.
L. Catledge, J. Pitkow. Characterizing browsing behaviors on the World Wide Web, Computer Networks and ISDN Systems 27(6), 1995.
M.S. Chen, J.S. Park, P.S. Yu. Data mining for path traversal patterns in a Web environment. In: Proc. 16th International Conference on Distributed Computing Systems, 1996, pp. 385-392.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.