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A Skipping FP-tree for Incrementally Intersecting Closed Itemsets in On-Line Stream Mining


Using Big Data Analytics to Cr

Class Agnostic Image Common Ob

3D Reconstruction in Canonical
Abstract


An on-line mining for a data stream consisting of large transactions is still quite difficult because of an explosion of frequent itemsets. In this paper, we propose a new data structure, called a skipping FP-tree, which enables us to effectively compress the set of closed itemsets in a stream. We show, through experimental evaluations, the skipping FP-tree achieves more than ten times faster computation for incremental intersections.

KeyWords
data mining, stream, closed itemset, FP-tree, intersection, compression, skip



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