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ACS/IEEE International Conference on Computer Systems and Applications (AICCSA'01)   p. 0144
Discovery of Fuzzy Sequential Patterns for Fuzzy Partitions in Quantitative Attributes
Ruey-Shun Chen, National Chiao Tung University
Gwo-Hshiung Tzeng, National Chiao Tung University
C.C. Chen, National Chiao Tung University
Yi-Chung Hu, National Chiao Tung University

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AICCSA.2001.933967
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Abstract
Abstract: In this paper, we propose the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) to generate all fuzzy sequential patterns from relational database. In FGBSPMA, each quantitative attribute is viewed as a linguistic variable, and can be divided into many candidate 1-dim fuzzy grids. FGBSPMA is consisted of two phases: one is to generate all the large 1-fuzzy sequences, the other is to generate all the fuzzy sequential patterns. FGBSPMA is a efficiently fuzzy sequential patterns mining algorithm, because FGBSPMA scans database only once and applies proper operations on rows of tables to generate large fuzzy sequences and fuzzy sequential patterns. An example is given to illustrate a detailed process for mining the fuzzy sequential patterns from a specified relation. From this example, we can show efficiency and usefulness of FGBSPMA.
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Index Terms- Data mining; Database; Fuzzy sequential patterns; Fuzzy partitions; Knowledge acquisition Citation:  Ruey-Shun Chen, Gwo-Hshiung Tzeng, C.C. Chen, Yi-Chung Hu, "Discovery of Fuzzy Sequential Patterns for Fuzzy Partitions in Quantitative Attributes," aiccsa, p. 0144,  ACS/IEEE International Conference on Computer Systems and Applications (AICCSA'01),  2001