When classifying, training data is used to build a model, and then it is tested with a presumably separate testing dataset. When classifying data, it is often useful to use only a subset of the training data that is relevant to the testing data. Relevancy is usually defined as being similar\footnote{This can be either some Euclidean distance or some other distance metric}, or local, to the testing data. Relevancy can be determined by segmenting the training data using a set of criteria, using a clustering algorithm, or using instance based reasoning. \tion{locallearners} will explore several locality based learners; \tion{lwl} will specifically describe an instance based reasoning algorithm. \tion{clustering} will explore finding local data with clustering.
