PARALLEL FUZZY FREQUENT ITEMSET MINING USING CELLULAR AUTOMATA

Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for association rule mining the context of data mining. If are detected, decision-making process and formulating strategies businesses will be made more precise. Because characteristic these models large number transactions unlimited high-speed productions. This leads to limitations calculating support containing attributes. As result, using parallel processing techniques has emerged as potential solution issue slow availability. study presents reinforced technique sets based on cellular learning automata (CLA). The results demonstrate that set can accomplished with less running time when proposed method compared iMFFP NPSFF methods.