Clustering with a Domain - Speci cDistance

With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set objects , by clustering noisy point-set images. Unlike traditional clustering methods which use distance measures that operate on feature vectors { a representation common to most problem domains { this object-based clustering technique employs a distance measure spe-ciic to a type of object within a problem domain. Formulating the clustering problem as two nested objective functions, we derive optimization dynamics similar to the Expectation-Maximization algorithm used in mixture models.