Abstract

This paper describes an algorithm for orthogonal clustering. That is, it nds multiple partitions of a domain. The Minimum Description Length (MDL) Principle is used to de ne a parameter-free evaluation function over all possible sets of partitions. In contrast, conventional clustering algorithms can only nd a single partition of a set of data. While they can be applied iteratively to create hierarchies, these are limited to tree structures. Orthogonal clustering, on the other hand, cannot form hierarchies deeper than one layer. Ideally one would want an algorithm which doesboth. However there are important problems for which orthogonal clustering is desirable. In particular, orthogonal clusters correspond to feature vectors, which are widely used throughout cognitive science. Hopefully, orthogonal clusters will also be useful for nding analogies. A side e ect which deserves more exploration is the induction of domain axioms in which the features

Citations