Clustering with a Domain-Specific Distance Measure

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Steven Gold, Eric Mjolsness, Anand Rangarajan


With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set ob(cid:173) jects, by clustering noisy point-set images. Unlike traditional clus(cid:173) tering 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(cid:173) cific 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.