Association is a technique in data mining used to identify the relationship between itemsets in a database (association rule). Some researches in association rule since the invention of AIS algorithns. Some of those used artificial datasets (IBM) and claimed by the authors to have a reliable performance in finding maximal frequent itemset. But these datasets have a different characteristics from world dataset. The goal of this research is to compare the performance of Apriori and Cut Both Ways (CBW) algorithms using 3 real world datasets. We used small and large values of minimum support thresholds as atreatment for each algorithm and datasets. As a result we find that the characteristics of datasets have a signifcant effect on the performance of Apriori and CBW. Support counting strategy, horizontal counting, showed a better performance compared of vertical intersection although condidate frequent itemsets counted was fewer.