Temporal association rules with numerical attributes (1999) [1 citations — 1 self]
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Abstract:
Data mining has been an area of increasing interest during recent years. The association rule discovery problem in particular has been widely studied. However, there are still some unresolved problems. For example, research on mining patterns in the evolution of numerical attributes is still lacking. This is both a challenging problem and one with significant practical application in business, science, and medicine. In this paper, we present a parameterizable model for temporal sequences of numerical attributes and devise efficient ways to search for parameter values that will result in a good fit to (at least a significant portion of) the data. Metrics for how well instances of the model fit portion of the data include the familiar measures of support and strength used in association rule mining and a new metric called density. A user specifies thresholds for these metrics and, based on structural properties of the class of models we are attempting to fit to the data, the search space can be drastically pruned using these thresholds. Experimental results on real and synthetic data sets demonstrate the efficiency of our algorithm. 1
