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13.355 Articles in journals published by the UPC
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  • 2008, Vol. XV, núm. 1
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Fuzzy sequential pattern mining in incomplete databases

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Fiot, Céline
Laurent, Anne
Teisseire, Maguelonne
Document typeArticle
Defense date2008
PublisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Recent widening of data mining application areas have lead to new issues. For instance, frequent sequence discovery techniques that were developed for customer behaviour analysis are now applied to analyse industrial or biological databases. Thus frequent sequence mining algorithm must be adapted to handle particular characteristics of these data. Among these specificities one should consider numerical attributes and incomplete data. In this paper, we propose a method for discovering crisp or fuzzy sequential patterns from an incomplete database. This approach uses partial information contained in incomplete records, only temporary discarding the missing part of the record. Experiments run on various synthetic datasets show the validity of our proposal as well in terms of quality as in terms of the robustness to the rate of missing values.
CitationFiot, Céline; Laurent, Anne; Teisseire, Maguelonne. Fuzzy sequential pattern mining in incomplete databases. "Mathware & Soft Computing", vol. 15, núm. 1, p. 41-59. 
URIhttp://hdl.handle.net/2099/13166
ISSN1134-5632
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  • Mathware & soft computing - 2008, Vol. XV, núm. 1 [8]
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