A new approach for discovering fuzzy quantitative sequential patterns in sequence databases

Yen Liang Chen, Tony Cheng Kui Huang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Given a sequence database and minimum support threshold, the goal of mining quantitative sequential patterns is to discover the complete set of sequential patterns with purchased quantities in databases. Although this type of pattern can provide more information than the traditional sequential pattern, it also causes a sharp boundary problem. This means that when an item's quantity is close to the boundary of two adjacent quantity intervals, it is either ignored or overemphasized. In view of this weakness, a recent paper from Hong, Kuo, and Chi proposed a new kind of extended patterns, called fuzzy quantitative sequential patterns (FQSP), where an item's quantity in the pattern is represented by a fuzzy term rather than a quantity interval. In their work an Apriori-like algorithm was developed to mine all FQSP. In this paper, we propose a new and novel algorithm to mine FQSP based on the divide-and-conquer strategy. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous algorithm.

Original languageEnglish
Pages (from-to)1641-1661
Number of pages21
JournalFuzzy Sets and Systems
Issue number12
StatePublished - 16 Jun 2006


  • Data mining
  • Fuzzy sets
  • Quantitative data
  • Sequence data
  • Sequential patterns


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