From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles IEEE Transactions on Fuzzy Systems Year : 2007

From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining

Abstract

Most real world databases consist of historical and numerical data such as sensor, scientific or even demographic data. In this context, classical algorithms extracting sequential patterns, which are well adapted to the temporal aspect of data, do not allow numerical information processing. Therefore the data are pre-processed to be transformed into a binary representation, which leads to a loss of information. Fuzzy algorithms have been proposed to process numerical data using intervals, particularly fuzzy intervals, but none of these methods is satisfactory. Therefore this paper completely defines the concepts linked to fuzzy sequential pattern mining. Using different fuzzification levels, we propose three methods to mine fuzzy sequential patterns and detail the resulting algorithms (SPEEDYFUZZY, MINIFUZZY and TOTALLYFUZZY). Finally, we assess them through different experiments, thus revealing the robustness and the relevancy of this work.
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lirmm-00195099 , version 1 (18-11-2019)

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Céline Fiot, Anne Laurent, Maguelonne Teisseire. From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining. IEEE Transactions on Fuzzy Systems, 2007, 15 (6), pp.1263-1277. ⟨10.1109/TFUZZ.2007.894976⟩. ⟨lirmm-00195099⟩
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