Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: concepts and techniques
Data mining: concepts and techniques
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Web usage mining using support vector machine
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
| Hi-index | 0.00 |
The web logs contain the information of the user’s access record to a web site. The recommender system of the web site is improved by analyzing web log file including user’s duration time at each web page. The web usage mining is the application of data mining techniques to large web data repositories in order to extract usage patterns. Many algorithms have been proposed to construct recommender system in web usage mining. In general, the size of web log records is large. So we have difficulties to analyze web log data. To make matter worse, the web log data are very sparse. It is very hard to estimate the dependencies between the web pages. Therefore, we solved these problems of web usage mining using combined evolutionary computing into support vector machine. In this paper, we proposed a new mining model for web usage mining. We verified the performance of proposed model using two data sets from KDD Cup 2000 and our web server.