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