All proposed parallel algorithms for mining association rules follow the conventional level-wise approach.It imposes a synchronization in every iteration in the computation which degrades greatly their performance if they are used to compute the rules on a shared-memory multi-processor parallel machine.The deficiency comes from the contention on the shared I/O channel when all processors are accessing the channel synchronously in every iteration.An asynchronous algorithm APM has been proposed for mining association rules on shared-memory multi-processor machine.All participating processors in APM generate candidates and count their supports independently without synchronization.Furthermore,it can finish the computation with fewer passes of database scanning than required in the level-wise approach.An optimization technique has been developed to enhance APM so that its performance would be insensitive to the data distribution.Two variants of APM and the synchronous algorithm Count Distribution,which is a parallel version of the popular serial mining algorithm Apriori,have been implemented on an SGI Power Challenge SMP parallel machine.The results show that the asynchronous algorithm APM performs much better,and is more scalable than the synchronous algorithm.