Abstract: Traditional algorithms in mining sequence patterns only discover the frequent sequences satisfying the minimum support threshold minsup;however,these methods don't consider the importance of actual sequences. In order to mine the important sequence patterns satisfying users' many demands, this paper presented a new sequence fuzzy concept lattice model. The model firstly introduced the weight for each item in sequences. On the basis of the weight, we defined the weight of every sequence and the self-adaptive coefficient that may dynamically adjust the minimum support threshold minsup. And then the fuzzy formal context was extended to express sequences in brief. Making use of the sequcnce fuzzy formal context, Galois connection, sequence fuzzy conception and sequence fuzzy concept lattice were defined in the paper. At last, this article presented the incremental construction algorithm SeqFuzCL of the sequence fuzzy concept lattice. The experimental results show that the algorithm SeqFuzCL can effectively express self-adaptive sequcnce patterns in the lattice, and has excellent performance on the timcspatial complexity. Simultaneously, the model provides theoretic support for mining self-adaptive secauence patterns.

Key words: Sequence pattern, Fuzzy formal context, Fuzzy concept lattice, Incremental construction