AN IMPROVED K-NN APPROACH FOR AUTOMATED WEB USAGE MINING AND RECOMMENDATION SYSTEM
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Abstract
Web Usage Mining (WUM) is to discover interesting patterns of usage from web based data to understand it and to serve the needs of the web based applications in a considerable superior manner. Here in WUM technique, there is a use of Automated web usage data mining and the recommendation system (which works on analyzing the behavior of frequent user) using an improved K-Nearest Neighbor (K-NN) classification method (where it is essential to calculate distance, assigns pattern matching value and weight to calculate the contribution of the neighbors). In this propose a paper Manhattan distance (MD) based nearest neighbor approach is used for the class which is undefined. And also calculate the weights of matched fields with those of the pattern matching value and assign majority or maximum weighted neighbor as an undefined class label. Using With this distance based learning is becoming clear, decrease in computation time, it is easier to know which attribute can produce a better result, and so on.
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