Many data mining techniques consist in discovering patterns frequently occurring in the source dataset. Typically, the goal is to discover all the patterns whose frequency in the dataset exceeds a userspecified threshold. However, very often users want to restrict the set of patterns to be discovered by adding extra constraints on the structure of patterns. Data mining systems should be able to exploit such constraints to speed-up the mining process. In this paper, we focus on improving the efficiency of constraint-based frequent pattern mining by using dataset filtering techniques. Dataset filtering conceptually transforms a given data mining task into an equivalent one operating on a smaller dataset. We present transformation rules for various classes of patterns: itemsets, association rules, and sequential patterns, and discuss implementation issues regarding integration of dataset filtering with well-known pattern discovery algorithms.