Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Data & Knowledge Engineering
Visualizing and fuzzy filtering for discovering temporal trajectories of association rules
Journal of Computer and System Sciences
Discovering richer temporal association rules from interval-based data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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In this paper, an algorithm for mining association rules is proposed that is based on the generation of multiple decision trees and extraction of rules from them. This method is quite effective especially in data sets that contain numeric attributes. In this paper, also, it is studied the capturing of the evolution of association rules during time. Since most of the interesting observations involve time, the evolution of association rules during time is quite important. In order to capture and study this evolution, the notion of temporal rules is proposed and a method for mining them is described. Finally, methods for visualisation of temporal rules are proposed in order to offer to the users the opportunity to perform comparisons of support and confidence of consecutive temporal periods easily.