Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
| Hi-index | 0.00 |
It is an important part of research content in data mining to discover association rules from large scale database, the main problem of which is frequent itemsets mining. The classical Apriori algorithm is an efficient one for that. Aimed at the performance bottlenecks of multiply scanning the database and generating a large quantity of candidate itemsets in Apriori algorithm, an improved algorithm of mining association rules is presented for the bottleneck problem. Filtering out the transactions unconcerned with mining targets by a presupposed filter, on the one hand, the improved Apriori algorithm can compresses database and reduces scanning times; on another hand, the number of candidate itemsets also decreases with it, so the improvement strategy can greatly improves the whole performance of the algorithm. The application of improved Apriori algorithm in traffic accident data mining also shows that it is very practical and efficient in data mining.