Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Most algorithms for frequent pattern mining use a support constraint to prune the combinatorial search space but support-based pruning is not enough. After mining datasets to obtain frequent patterns, the resulting patterns can have weak affinity. Although the minimum support can be increased, it is not effective for finding correlated patterns with increased weight and/or support affinity. Interesting measures have been proposed to detect correlated patterns but any approach does not consider both support and weight. In this paper, we present a new strategy, Weighted interesting pattern mining (WIP) in which a new measure, weight-confidence, is suggested to mine correlated patterns with the weight affinity. A weight range is used to decide weight boundaries and an h-confidence serves to identify support affinity patterns. In WIP, without additional computation cost, original h-confidence is used instead of the upper bound of h-confidence for performance improvement. WIP not only gives a balance between the two measures of weight and support, but also considers weight affinity and/or support affinity between items within patterns so more correlated patterns can be detected. To our knowledge, ours is the first work specifically to consider weight affinity between items of patterns. A comprehensive performance study shows that WIP is efficient and scalable for finding affinity patterns. Moreover, it generates fewer but more valuable patterns with the correlation. To decrease the number of thresholds, w-confidence, h-confidence and weighted support can be used selectively according to requirement of applications.
correlation weight affinity h-confidence data mining w-confidence knowledge discovery performance evaluation