Fuzzy Sequential Pattern Mining In Incomplete Databases

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.
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Céline Fiot, Anne Laurent, Maguelonne Teisseire. Fuzzy Sequential Pattern Mining In Incomplete Databases. Mathware & soft computing, RACO, 2008, 15 (1), pp.41-59. ⟨lirmm-00273928⟩

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