On Measuring Similarity for Sequences of Itemsets

Elias Egho 1 Chedy Raïssi 1 Toon Calders 2 Nicolas Jay 1 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Computing the similarity between sequences is a very important challenge for many different data mining tasks. There is a plethora of similarity measures for sequences in the literature, most of them being designed for sequences of items. In this work, we study the problem of measuring the similarity between sequences of itemsets. We present new combinatorial results for efficiently counting distinct and common subsequences. These theoretical results are the cornerstone of an effective dynamic programming approach to deal with this problem. Experiments on healthcare trajectories and synthetic datasets, show that our measure of similarity produces competitive scores and indicates that our method is relevant for large scale sequential data analysis.
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Elias Egho, Chedy Raïssi, Toon Calders, Nicolas Jay, Amedeo Napoli. On Measuring Similarity for Sequences of Itemsets. [Research Report] RR-8086, INRIA. 2012, pp.19. ⟨hal-00740231v2⟩

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