This paper presents a pattern mining method with constraints for mean value of numeric attribute by constraint satisfaction probability. From the standpoint of usability, it is sometimes required that a method quickly mine patterns satisfying the constraint, although it cannot enumerate them completely. Constraint satisfaction probability is a ratio of constraint satisfaction pattern to the possible super pattern. We attempt to decide search priority by constraint satisfaction probability in sequential pattern mining. In our experimental evaluation, we show that pattern mining by constraint satisfaction probability is a little more effective than simple heuristics.