Association rules over interval data

We consider the problem of mining association rules over interval data (that is, ordered data for which the separation between data points has meaning). We show that the measures of what rules are most important (also called rule interest) that are used for mining nominal and ordinal data do not capture the semantics of interval data. In the presence of interval data, support and confidence are no longer intuitive measures of the interest of a rule. We propose a new definition of interest for association rules that takes into account the semantics of interval data. We developed an algorithm for mining association rules under the new definition and overview our experience using the algorithm on large real-life datasets.

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