Implementation of classification technique in web usage mining of banking company

The goals of this paper is to implement classification technique in web usage mining to a bank company that can help the company to identify web performance issue. Web usage mining consists of three phases: data preprocessing, pattern discovery, and pattern analysis. In pattern discovery phase, we propose to use classification technique with k-nearest neighbor algorithm implemented with standardized Euclidean distance to classifying frequent access pattern. The result shows that the k-nearest neighbor algorithm can be implemented in web usage mining and can help company to find interesting knowledge in web server log.