A novel knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases

Items sold in a store can usually be organized into a concept hierarchy according to a taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies had crisp relationships between the categories in one level and the categories in another level. In real life, however, crisp taxonomies cannot handle the uncertainties and fuzziness inherent in the relationships among items and categories. For example, the book Alice’s Adventures in Wonderland can be classified into the Children’s Literature category, but can also be related to the Action & Adventure category. To deal with the fuzzy nature of taxonomy, we apply fuzzy set techniques to concept taxonomies so that the relationships from one level to another can be represented by a value between 0 and 1. Accordingly, a fuzzy multiple-level mining algorithm, the fuzzy multi-level sequential mining algorithm (FMSM), is proposed to extract fuzzy multiple-level sequential patterns from databases. In addition, another algorithm, named the CROSS-FMSM algorithm, is developed to discover fuzzy cross-level sequential patterns. Experiments using synthetic datasets show the algorithms’ computational efficiency and scalability, and a real dataset is used to prove the patterns’ effectiveness.