Abstract
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.
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References
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Koundourakis, G., Theodoulidis, B. (2002). Association Rules & Evolution in Time. In: Vlahavas, I.P., Spyropoulos, C.D. (eds) Methods and Applications of Artificial Intelligence. SETN 2002. Lecture Notes in Computer Science(), vol 2308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46014-4_24
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DOI: https://doi.org/10.1007/3-540-46014-4_24
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