Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data

Abstract

Web usage mining, is the method of mining for user browsing and access patterns. Usage data captures the identity or origin of Web users along with their surfing behavior at a Web site. This paper aims to classify user behavior in identifying the patterns of the browsing and navigation data of web users and also measure the performance of the Frequent Pattern (FP) Growth algorithm and Apriori algorithm by comparing their performances. The Apriori algorithm and FP Growth algorithm are compared by applying the rapid miner tool to discover frequent user patterns along with user behavior in the web log. Both the algorithms help to the analyze the patterns of web site usage and the features of user behavior knowledge obtained from web usage. This can be used to enhance web design, introduce personalization service and facilitate more effective browsing. The experimental results mainly focus on number of instance and execution time to be calculated on the two algorithms. FP growth algorithm gives the better performance in terms of time complexity.