Discovery of fuzzy sequential patterns for fuzzy partitions in quantitative attributes

Author

Ruey-Shun Chen ; Tzeng, Gwo-Hshiung ; Chen, C.C. ; Hu, Yi-Chung

Author_Institution

Inst. of Inf. Manage., Nat. Chiao Tung Univ., Hsinchu, Taiwan

fYear

2001

fDate

2001

Firstpage

144

Lastpage

150

Abstract

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

Keywords

data mining; database theory; fuzzy logic; pattern recognition; relational databases; FGBSPMA; Fuzzy Grid Based Sequential Pattern Mining Algorithm; data mining; fuzzy partitions; fuzzy sequential pattern discovery; knowledge acquisition; linguistic variable; quantitative attributes; relational databases; Association rules; Data mining; Databases; Expert systems; Fuzzy systems; Hybrid intelligent systems; Information management; Knowledge acquisition; Knowledge representation; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Systems and Applications, ACS/IEEE International Conference on. 2001

Conference_Location

Beirut

Print_ISBN

0-7695-1165-1

Type

conf

DOI

10.1109/AICCSA.2001.933967

Filename

933967