Interestingness-based interval merger for numeric association rules
Ke Wang, Soon Hock William Tay, Bing Liu · 1998
We present an algorithm for mining association rules from relational tables containing numeric and categorical attributes. The approach is to merge adjacentintervals of numeric values, in a bottom-up manner, on the basis of maximizing the interestingness of a set of association rules. A modi cation of the B-tree is adopted for performing this task e ciently. The algorithm takes O(kN) I/O time, where k is the number of attributes and N is the numberofrows in the table. We evaluate the e ectiveness of producing good intervals.