Web User Access Behavior Analysis & Prediction. An Association Rule mining Approach using Map-Reduce Framework on Apache Spark

      Аннотация: The analysis of Web users browsing behaviors is essential for putting appropriate information on the web. The browsing behaviors are stored as navigational patterns in web server logs. These weblogs are used to predict the frequently accessed patterns of web users, which can be used to predict user behavior and to collect business intelligence. However, owing to the exponentially increasing weblog size, existing implementations of frequent pattern mining algorithms often takes too much time and generate too many redundant patterns. This book discussed new association rule mining algorithms implemented on the Apache Spark platform for extracting frequent patterns from huge weblogs.

Ключевые слова: Apache Spark, Association Rule Mining, confidence, Leverage, Lift, Map-Reduce, Support, Web User, Access Behavior, Redundent Rules, Non-Informative Rules, Interestingness Measures, cosine and conviction