In constrained data mining, users can specify constraints to prune the search space to avoid mining uninteresting knowledge. This is typically done by specifying some initial values of the constraints that are subsequently refined iteratively until satisfactory results are obtained. Existing mining schemes treat each iteration as a distinct mining process, and fail to exploit the information generated between iterations. In this paper, we propose to salvage knowledge that is discovered from an earlier iteration of mining to enhance subsequent rounds of mining. In particular, we look at how frequent patterns can be recycled. Our proposed strategy operates in two phases. In the first phase, frequent patterns obtained from an early iteration are used to compress a database. In the second phase, subsequent mining processes operate on the compressed database. We propose two compression strategies and adapt three existing frequent pattern mining techniques to exploit the compressed database. Results from our extensive experimental study show that our proposed recycling algorithms outperform their non-recycling counterpart by an order of magnitude.