Discovery of fuzzy sequential patterns for fuzzy partitions in quantitative attributes
Ruey‐Shun Chen, Gwo‐Hshiung Tzeng, C.C. Chen, Yi‐Chung Hu · 2002
We propose the Fuzzy Grid Based Sequential Pattern Mining Algorithm (FGBSPMA) to generate all fuzzy sequential patterns from relational databases. In FGBSPMA, each quantitative attribute is viewed as a linguistic variable, and can be divided into many candidate 1-dim fuzzy grids. FGBSPMA consists 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 an efficient fuzzy sequential pattern mining algorithm, because FGBSPMA scans the 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 show the efficiency and usefulness of FGBSPMA.