The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. The goal of this research was to develop and implement a parallel algorithm for mining association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. We implemented the DDRM using a dynamic load balancing approach to assign classes to processors for analysis of these classes in order to determine if there are any rules present in them. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and Partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup.