Distance-based algorithms for both clustering and prediction are popular within the machine learning community. These algorithms typically deal with attributevalue (single-table) data. The distance functions used are typically hard-coded.

We are concerned here with generic distance-based learning algorithms that work on arbitrary types of structured data. In our approach, distance functions are not hard-coded, but are rather first-class citizens that can be stored, retrieved and manipulated. In particular, we can assemble, on-the-fly, distance functions for complex structured data types from pre-existing components.

To implement the proposed approach, we use the strongly typed functional language Haskell. Haskell allows us to explicitly manipulate distance functions. We have produced a SW library/application with structured data types and distance functions and used it to evaluate the potential of Haskell as a basis for future work in the field of distancebased machine learning.