Active topographic mapping of proximities

We deal with the question of how to reduce the computational costs of obtaining and clustering dissimilarity data. We show that for pairwise clustering, a large portion of the dissimilarity data can be neglected without incurring a serious deterioration of the clustering solution. This fact can be exploited by selecting the dissimilarity values that are supposed to be most relevant in a well-directed manner. We present an algorithm for active data selection for topographic pairwise clustering that aims at maximizing the expected reduction in the clustering cost function and propose a computationally more efficient approximation to this algorithm that yields satisfactory results in cases where the topography is imposed only weakly