Adaptation is the least well studied process in case-based reasoning (CBR). The main reasons for this are the potentially complex nature of implementing adaptation knowledge and the difficulties associated with acquiring quality knowledge in the first place and competently maintaining it over time. For these reasons most CBR systems are designed to leave the adaptation component to the expert and therefore function simply as case retrieval systems as opposed to truly reasoning systems. Here we present a competent adaptation strategy, which uses a modified regression algorithm to automatically discover and implement locally specific adaptation knowledge in CBR. The advantages of this approach are that the adaptation knowledge acquisition process is automated, localised, guaranteed to be specific to the task at hand, and there is no adaptation knowledge maintenance burden on the system. The disadvantage is that the time taken to form solutions is increased but we also show how a novel indexing scheme based on k-means clustering can help reduce this overhead considerably.