Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Indirect Association: Mining Higher Order Dependencies in Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Indirect Associations in Web Data
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of indirect associations using HI-mine
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Proportional fault-tolerant data mining with applications to bioinformatics
Information Systems Frontiers
Mining Indirect Association Rules for Web Recommendation
International Journal of Applied Mathematics and Computer Science
A generic approach for mining indirect association rules in data streams
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Hi-index | 0.00 |
This paper presents a novel pattern called temporal indirect association. An indirect association pattern refers to a pair of items that rarely occur together but highly depend on the presence of a mediator itemset. The existing model of indirect association does not consider the lifespan of items. Consequently, some discovered patterns may be invalid while some useful patterns may not be covered. To overcome this drawback, in this paper, we take into account the lifespan of items to extend the current model to be temporal. An algorithm, MG-Growth, that finds the set of mediators in pattern-growth manner is developed. Then, we extend the framework of the algorithm to discover temporal indirect associations. Our experimental results demonstrated the efficiency and effectiveness of the proposed algorithms.