%0 Generic %1 harishkumar2013advance %A Harish Kumar, Dr. Anil Kumar Solanki %B 2013 Mobile Communication - I %D 2013 %E Kaushik, Dr. B K %I ACEEE (A Computer division of IDES) %K (Fuzzy FPCM Markov Web chain clustering logs %T Advance Clustering Technique Based on Markov Chain for Predicting Next User Movement %U http://searchdl.org/public/book_series/LSCS/2/563.pdf %X Aim: According to the survey India is one of the leading countries in the word for technical education and management education. Numbers of students are increasing day by day by the growth rate of 45% per annum. Advancement in technology puts special effect on education system. This helps in upgrading higher education. Some universities and colleges are using these technologies. Weblog is one of them. Main aim of this paper is to represent web logs using clustering technique for predicting next user movement and user behavior analysis. This paper moves around the web log clustering technique based on Markov chain results .In this paper we present an ideal approach to web clustering (clustering web site users) and predicting their behavior for next visit. Methodology: For generating effective result approx 14 engineering college web usage data is used and an advance clustering approach is presenting after optimizing the other clustering approach.Results: The user behavior is predicted with the help of the advance clustering approach based on the FPCM and k-mean. Proposed algorithm is used to mined and predict user’s preferred paths. To predict the user behavior existing approaches have been used. But the existing approaches are not enough because of its reaction towards noise. Thus with the help of ACM, noise is reduced, provides more accurate result for predicting the user behavior. Approach Implementation:The algorithm was implemented in MAT LAB, DTRG and in Java .The experiment result proves that this method is very effective in predicting user behavior. The experimental results have validated the method’s effectiveness in comparison with some previous studies

@conference{harishkumar2013advance, abstract = {Aim: According to the survey India is one of the leading countries in the word for technical education and management education. Numbers of students are increasing day by day by the growth rate of 45% per annum. Advancement in technology puts special effect on education system. This helps in upgrading higher education. Some universities and colleges are using these technologies. Weblog is one of them. Main aim of this paper is to represent web logs using clustering technique for predicting next user movement and user behavior analysis. This paper moves around the web log clustering technique based on Markov chain results .In this paper we present an ideal approach to web clustering (clustering web site users) and predicting their behavior for next visit. Methodology: For generating effective result approx 14 engineering college web usage data is used and an advance clustering approach is presenting after optimizing the other clustering approach.Results: The user behavior is predicted with the help of the advance clustering approach based on the FPCM and k-mean. Proposed algorithm is used to mined and predict user’s preferred paths. To predict the user behavior existing approaches have been used. But the existing approaches are not enough because of its reaction towards noise. Thus with the help of ACM, noise is reduced, provides more accurate result for predicting the user behavior. Approach Implementation:The algorithm was implemented in MAT LAB, DTRG and in Java .The experiment result proves that this method is very effective in predicting user behavior. The experimental results have validated the method’s effectiveness in comparison with some previous studies}, added-at = {2014-02-05T08:05:26.000+0100}, author = {Harish Kumar, Dr. Anil Kumar Solanki}, biburl = {https://www.bibsonomy.org/bibtex/2687aa35e62b4a0d18ff7bff26c08f4ba/idescitation}, booktitle = {2013 Mobile Communication - I}, editor = {Kaushik, Dr. B K}, interhash = {cd46287c28339d2355e367772c56b1f5}, intrahash = {687aa35e62b4a0d18ff7bff26c08f4ba}, keywords = {(Fuzzy FPCM Markov Web chain clustering logs}, organization = {Institute of Doctors Engineers and Scientists}, publisher = {ACEEE (A Computer division of IDES)}, timestamp = {2014-02-05T08:05:26.000+0100}, title = {Advance Clustering Technique Based on Markov Chain for Predicting Next User Movement}, url = {http://searchdl.org/public/book_series/LSCS/2/563.pdf}, year = 2013 }