Improved speculative Apriori with percentiles algorithm for website restructuring based on usage patterns

Abstract

Web structure mining techniques are popularly used in the process of improved website design/replanning based on user browsing actions. In this paper, an algorithm for improving the design map (site map of a Website) using the pertinent information available in the website's server logs is proposed, that incorporates probability for extending the well-known Apriori Algorithm. The proposed methodology harnesses the normal distribution curve used in statistical measurements to improve recommendation accuracy after parsing the server log file. This allows the discovery of more association rules as the idea is to use percentile calculations instead of the percentages and having a relative quest within the item sets to determine their existence in the domain. By enforcing the percentile calculations on the distribution curve of the collection, selective items from the small groups within can be obtained. Experimental results for the proposed Speculative Apriori with Percentiles Algorithm (SAwP) indicate that it was effective in discovering relevant itemsets and more association rules, when compared to classical Apriori algorithm.