Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10660
Title: Temporal Support in Sequential Pattern Mining
Authors: Gomez, Letitia
KUIJPERS, Bart 
VAISMAN, Alejandro 
Issue Date: 2009
Publisher: CEUR Workshop Proceedings
Source: Arenas, Marcelo & Bertossi, Leopoldo (Ed.) Proceedings of the Alberto Mendelzon Workshop on Foundations of Data Management (AMW'09). p. 1-10.
Abstract: In sequential pattern discovery, the support of a sequence is computed as the number of data-sequences satisfying a pattern with respect to the total number of data-sequences in the database. When the items are frequently updated, the traditional way of counting support in sequential pattern mining may lead to incorrect (or, at least incomplete), conclusions. For example, if we are looking for the support of the sequence A.B, where A and B are two items such that A was created after B, all sequences in the database that were completed before A was created, can never produce a match. Therefore, accounting for them would underestimate the support of the sequence A.B. In this paper we propose to revise the classic notion of support in sequential pattern mining, introducing the concept of temporal support of a sequential expression (SE), intuitively defined as the number of sequences satisfying a target pattern, out of the total number of sequences that could have possibly matched such pattern. We then generalize this notion to regular expressions (RE) which encapsulate the definition of a collection of SEs. We present and discuss a theoretical framework for these novel notion of support, and present an algorithm to compute it.
Document URI: http://hdl.handle.net/1942/10660
Link to publication: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-450/paper1.pdf
Category: C2
Type: Proceedings Paper
Appears in Collections:Research publications

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