In view of the defects of Apriori association rule mining algorithm needed to scan database frequently and generate a set of large candidate. In this paper, the binary string was used to express information granule, using grain-bit binary extraction frequent item sets and discovering association rules. Through experiments, the classical Apriori algorithm and the algorithm that based on granular computing association rules extraction were compared to the experimental results are analyzed. The results show that the algorithm based on granular computing for mining association rules is feasible and effective.
Xiaojun REF includes grain bit binary extraction to mine frequent itemsets .
An Algorithm of Mining Association Rules Based on Granular Computing
Physics Procedia
Physics