Mining fuzzy sequential patterns from quantitative data

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the conventional data mining algorithms can identify the relationships among transactions with binary values. Temporal transactions with quantitative values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new data mining algorithm, which takes advantage of fuzzy set theory to enhance the capability of exploring interesting sequential patterns from databases with quantitative values. The proposed algorithm integrates the concepts of fuzzy sets and the AprioriAll algorithm to find interesting sequential patterns and fuzzy association rules from transaction data