Clustering on categorical data streams is a relatively new field that has not received as much attention as static data and numerical data streams. One of the main difficulties in categorical data analysis is lacking in an appropriate way to define the similarity or dissimilarity measure on data. In this paper, we propose three dissimilarity measures: a point-cluster dissimilarity measure (base...

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in some instances matching two segments of points may be preferred and can result in increased noise robustness. This paper presents an approach to segmental sequence alignment based on adaptive pairwise segmentation. We introduce a distance metric be...

Abstract Content-based image retrieval (CBIR) methods search for points with the most similar content to query features from within a large dataset. The notable approach this purpose is an approximate nearest neighbor (ANN) searching. main properties expected system can be listed as follows; low storage requirement, high retrieve speed, and average precision. Hashing, which generate discriminat...

Self-similarity was recently introduced as a measure of inter-class congruence for classification of actions. Herein, we investigate the dual problem of intra-class dissimilarity for classification of action styles. We introduce self-dissimilarity matrices that discriminate between same actions performed by different subjects regardless of viewing direction and camera parameters. We investigate...

Several researchers have developed properties that ensure compatibility of a concept similarity or dissimilarity measure with the formal semantics of Description Logics. While these authors have highlighted the relevance of the triangle inequality, none of their proposed dissimilarity measures satisfy it. In this work we present a theoretical framework for dissimilarity measures with this prope...

The paper deals with the well-known notion of (dis)similarity measures between fuzzy sets. We provide three separate lists of axioms that fit with the respective notions of “general comparison measure”, “similarity measure” and “dissimilarity measure”. Then we review some of the most important axiomatic definitions of (dis)similarity measures in the literature, by referring to the axioms in tho...

One of the biggest bottlenecks in supervised learning is its high labeling cost. To overcome this problem, we propose a new weakly-supervised learning setting called SU classification, where only similar (S) data pairs (two examples belong to the same class) and unlabeled (U) data are needed, instead of fully-supervised data. We show that an unbiased estimator of the classification risk can be ...

This is an introductory tutorial on distance and similarity measures. In information retrieval, a distance is a metric that denotes dissimilarity or lack of resemblance while similarity is a measure of resemblance.

We are interested in supervised metric learning of Mahalanobis like distances. Existing approaches mainly focus on learning a new distance using similarity and dissimilarity constraints between examples. In this paper, instead of bringing closer examples of the same class and pushing far away examples of different classes we propose to move the examples with respect to virtual points. Hence, ea...