Paper Title
Comparison of Enhanced Ada Boost Algorithm With Decision Tree For Web Related Data

Abstract
Enormous growth of World Wide Web increases the complexity for users to browse effectively. This paper throws ight on the web mining concept and its techniques taxonomy, explains the importance of web personalization concept and web usage mining procedure in detail. In addition, the paper explains he classification algorithms- Ada Boost Algorithm, Decision Tree and AODE. The paper takes a step ahead in this direction and proposes an enhanced Ada Boost Algorithm. Ada Boost, Decision Tree and Enhanced Ada Boost algorithms have been imulated and their results have been compared in terms of accuracy rate and other parameters. The results show that Enhanced version shows better performance and accuracy than he Ada Boost and Decision Tree Algorithm. Index Termsó web data mining, information retrieval, Web usage mining, Pre-processing, Pattern Analysis, Content Mining; Structure Mining, Classification.