Similarity: measure of how close to each other two instances are. The “closer” the instances are to each other, the larger is the similarity value. Dissimilarity: measure of how different two instances are. Dissimilarity is large when instances are very different and is small when they are close. Proximity: refers to either similarity or dissimilarity Distance metric: a measure of dissimilarity...

Domain specific (dis-)similarity or proximity measures, employed e.g. in alignment algorithms in bio-informatics, are often used to compare complex data objects and to cover domain specific data properties. Lacking an underlying vector space, data are given as pairwise (dis-)similarities. The few available methods for such data do not scale well to very large data sets. Kernel methods easily de...

We introduce relational variants of neural topographic maps including the selforganizing map and neural gas, which allow clustering and visualization of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix originates from an euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalentl...

Dimensions of the meaning attributed to facial expressions of emotion were studied in preschoolers (nineteen 4-year-olds, twenty-one 3-year-olds, and thirty-eight 2-year-olds) plus thirty adults. Subjects indicated the similarity or dissimilarity between different emotions by placing photographs of emotional facial expressions into preordained numbers of groups. For each age group, multidimensi...

We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm, which allow a clustering and mining of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalently formulate bat...

This paper presents a distance-based discriminative framework for learning with probability distributions. Instead of using kernel mean embeddings or generalized radial basis kernels, we introduce embeddings based on dissimilarity of distributions to some reference distributions denoted as templates. Our framework extends the theory of similarity of Balcan et al. (2008) to the population distri...

Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecisi...

Virtual sales assistants with human-like interfaces (“avatars”) help consumers make better choices with less cognitive effort in online shopping environments. Following similarity-attraction theory, such avatars must be similar to consumers’ ethnicity and gender to be liked and used. In contrast, dissimilarity-repulsion theory posits that dissimilarity causes consumers to avoid avatars that mis...