301 research outputs found
Interactive Constrained Association Rule Mining
We investigate ways to support interactive mining sessions, in the setting of
association rule mining. In such sessions, users specify conditions (queries)
on the associations to be generated. Our approach is a combination of the
integration of querying conditions inside the mining phase, and the incremental
querying of already generated associations. We present several concrete
algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second
International Conference on Knowledge Discovery and Data Mining (DaWaK 2000
A Tight Upper Bound on the Number of Candidate Patterns
In the context of mining for frequent patterns using the standard levelwise
algorithm, the following question arises: given the current level and the
current set of frequent patterns, what is the maximal number of candidate
patterns that can be generated on the next level? We answer this question by
providing a tight upper bound, derived from a combinatorial result from the
sixties by Kruskal and Katona. Our result is useful to reduce the number of
database scans
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