Fuzzy Tree Mining: Go Soft on Your Nodes
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
FTMnodes: Fuzzy tree mining based on partial inclusion
Fuzzy Sets and Systems
Sequential pattern mining algorithm for automotive warranty data
Computers and Industrial Engineering
Knowledge gathering of fuzzy multi-time-interval sequential patterns
Information Sciences: an International Journal
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
Information Sciences: an International Journal
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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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.