Mining fuzzy sequential patterns from multiple-item transactions
Tzung‐Pei Hong, Kuie-Ying Lin, Shyue-Liang Wang · 2002
Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm that is able to deal with various types of data presents a challenge to workers in this research field. Since sequential patterns are also very important for real-world applications, this paper focuses on finding fuzzy sequential patterns from quantitative data. A new mining algorithm is proposed, which integrates the fuzzy-set concepts and the AprioriAll algorithm. It first transforms quantitative values in transactions into linguistic terms, then filters them to find sequential patterns by modifying the AprioriAll mining algorithm. Each quantitative item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The patterns mined out thus exhibit the sequential quantitative regularity in databases and can be used to provide some Suggestions to appropriate supervisors.