Title :
From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining
Author :
Fiot, Céline ; Laurent, Anne ; Teisseire, Maguelonne
Author_Institution :
Univ. of Montpellier II, Montpellier
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.
Keywords :
data mining; fuzzy set theory; numerical analysis; MiniFuzzy method; SpeedyFuzzy method; TotallyFuzzy method; binary representation; databases; fuzzy algorithm; numerical information processing; soft sequential pattern mining; Fuzzy intervals; numerical data; sequential patterns;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
DOI :
10.1109/TFUZZ.2007.894976