Interestingness-based Interval Merger for Numeric Association Rules

Ke Wang, Soon Hock William Tay, and Bing Liu

We present an algorithm for mining association rules from relational tables containing numeric and categorical attributes. The approach is to merge adjacent intervals of numeric values, in a bottom-up manner, on the basis of maximizing the interestingness of a set of association rules. A modification of the B-tree is adopted for performing this task efficiently. The algorithm takes O(kN) I/O time, where k is the number of attributes and N is the number of rows in the table. We evaluate the effectiveness of producing good intervals.

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