From crispness to fuzziness: Three algorithms for soft sequential pattern mining
C Fiot, A Laurent, M Teisseire - IEEE Transactions on Fuzzy …, 2007 - ieeexplore.ieee.org
C Fiot, A Laurent, M Teisseire
IEEE Transactions on Fuzzy Systems, 2007•ieeexplore.ieee.orgMost 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 …
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 …
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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