Efficient Frequent Pattern Mining using Particle Swarm Optimization

Association rule mining is one of the vital data mining tasks to extract knowledge from the data. In the process of association rule mining the foremost step is to find the frequent itemset. The frequent itemset is used to generate association rules. In general brute –force approach is expensive because there are exponentially many rules that can be generated from the data set. So that support count is the point to determining the frequency of occurrence for every candidate itemset that survives the candidate pruning of the rule generation. In general, support count chooses randomly by the user, and this random choosing of support count may not extract better association rules every time. In this work, applied particle swam optimization technique to choose appropriate support count, in order to extract efficient association rules.