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A Condensed Representation of Itemsets for Analyzing their Evolution over Time

A Condensed Representation of Itemsets for Analyzing their Evolution over Time

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled 2009. Driven by the need to understand change within domains there is emerging research on methods which aim at analyzing how patterns and in particular itemsets evolve over time. In practice, however, these methods suffer from the problem that many of the observed changes in itemsets are temporally redundant in the sense that they are the side-effect of changes in other itemsets, hence making the identification of the fundamental changes difficult. As a solution we propose temporally closed itemsets, a novel approach for a condensed representation of itemsets which is based on removing temporal redundancies. We investigate how our approach relates to the well-known concept of closed itemsets if the latter would be directly generalized to account for the temporal dimension. Our experiments support the theoretical results by showing that the set of temporally closed itemsets is significantly smaller than the set of closed itemsets.

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