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Classification and Clustering of Web Log Data to Analyze User Navigation Patterns
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kannan, N. |
| Copyright Year | 2010 |
| Abstract | The information explosion in World Wide Web has increased the interest in Web usage mining techniques in both commercial and academic areas. Study of interested web users; provide valuable information for web designers to quickly respond to their individual needs and for the efficient organization of the website. Among the several approaches, like, Association rule mining, classification, clustering, to extract knowledge from users navigation data, this paper uses clustering and classification of log data to discover knowledge from web log files. The proposed algorithm uses Expectation Maximization (EM) clustering along with Maximum Likelihood classification for knowledge discovery from users navigation patterns. Experiments have been carried out in order to validate the proposed approach and evaluate the proposed algorithm. |
| File Format | PDF HTM / HTML |
| Volume Number | 1 |
| Alternate Webpage(s) | http://www.jgrcs.info/index.php/jgrcs/article/download/209/208 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
