Association rule mining is one of the most important is- sues in data mining. Apriori computation schemes greatly reduce the computation time by pruning the candidate item- set. However, a large computation time is required when the treated data are dense and the amount of data is large. With apriori methods, the problem of becoming incomputable cannot be avoided when the total number of items is large. On the other hand, bottom-up approaches such as artifi- cial life approaches are the opposite to of the top-down approaches of searches covering all transactions, and may provide new methods of breaking away from the complete- ness of searches in conventional algorithms. Here, an ar- tificial life data mining technique is proposed in which one transaction is considered as one individual, and association rules are accumulated by the interaction of randomly se- lected individuals. The proposed algorithm is compaired to other methods in application to a large-scale actual dataset, and it is verified that its performance is greatly superior to that of the method using transaction data virtually di- vided and that of apriori method by sampling approach, thus demonstrating its usefulness.