Abstract：An association rule mining algorithm based on random meeting is proposed to handle the inefficient problem of algorithms.During random meeting,two transactions are selected randomly from an original set,and then the intersection of the transactions is computed and taken as the elements of a new set.The frequent item mining on the original set can be instead on the new set by mapping the min support degree on the two sets.Due to the fact that the new set is smaller than the original one,and the statistical properties of the new set are similar to those of the original one,the complexity of the new algorithm can be reduced while getting the similar accuracy to existing algorithms.The new algorithm was evaluated based on the transactions of a supermarket.The experiment results showed that the new algorithm can improve the speed of frequent item-set mining by more than ten times while achieve the similar accuracy of the computing results compared with other algorithms.

Keyword：data mining;frequent item-set mining;random meeting algorithm;random meeting;minimum support degree;