Classification of Arabic Documents depending on Maximal Frequent Itemsets

Abstract

In this paper we introduced techniques for classifying Arabic documents depending on association rules built from maximal frequent itemsets. Parallel Maximal Itemset Miner Algorithm (PMIMA) adopted several conditions to prune search space parallelly introduced for extracting maximal frequent itemsets. Rule length, rule weight and rule majority are three classification methods exploited to classification Arabic documents. Comparing with classification results obtained depending on all frequent itemsets extracted by Apriori, we proved efficiency of ours approach.