Mining Association Rules with Static and Dynamic Behavior of Learner in the Internet

Abstract

Now a days several Number of users are depending on internet to do their routine tasks, because the world wide web providing several services required to the people. Here the main problem is the internet environment providing huge number of services so we need to find the behavior of the user in various dimensions. First we performed a study on static model of the learner. Second we performed a study on dynamic model of the learner. In general the Association rules are extracted from the market basket analysis problem with using the apriori algorithm. Here we concentrated mainly on the unification process and apriori algorithm was improved and we experimented the internet based learning and we present the experimental results.

A Hybrid Algorithm for Frequent Pattern Mining Using MapReduce Framework

“…x Apriori algorithm complexity [12]: Let n represent the data quantity being processed, m the frequent item, M the total item quantity removed from 1 frequent itemset, and N the total invalid frequent item quantity. Because the Apriori algorithm continually repeats a similar process, it has the format of the following exponential function:…”