Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A parameterised algorithm for mining association rules
ADC '01 Proceedings of the 12th Australasian database conference
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Incremental Association Rules with Generalized FP-Tree
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Fast mining erasable itemsets using NC_sets
Expert Systems with Applications: An International Journal
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, we present a mining algorithm to improve the efficiency of finding large itemsets. Based on the concept of prediction proposed in the (n,p) algorithm, our method considers the data dependency in the given transactions to predict promising and non-promising candidate itemsets. Our method estimates for each level a different support threshold that is derived from a data dependency parameter and determines whether an item should be included in a promising candidate itemset directly. In this way, we maintain the efficiency of finding large itemsets by reducing the number of scanning the input dataset and the number candidate items. Experimental results show our method has a better efficiency than the apriori and the (n,p) algorithms when the minimum support value is small.