Similarity and dissimilarity measures are widely used in many research areas and
applications. When a dissimilarity measure is used, it is normally required to be a distance …

This paper formulates properties of similarity measures. We list a number of similarity
measures, some of which are not well known (such as the Monge-Kantorovich metric), or …

This article presents a distance and angle similarity measure. The integrated similarity
measure takes the strengths of both the distance and direction of measured documents into …

In this paper two distinct similarity measures in a document vector space, the distance-based
and angle-based similarity measures, are compared, and a newly developed similarity …

In this paper, we present a general guideline to establish the relation between a distribution
model and its corresponding similarity estimation. A rich set of distance metrics, such as …

Similarity or distance between two objects plays a fundamental role in many data mining
tasks like classification and clustering. Categorical data, unlike numeric data, conceptually is …

Non-metric dissimilarity measures may arise in practice eg when objects represented by
sensory measurements or by structural descriptions are compared. It is an open issue …

The notion of similarity between objects finds use in many contexts, for example, in search
engines, collaborative filtering, and clustering. Objects being compared often are modeled …

Statistical learning algorithms often rely on the Euclidean distance. In practice, non-
Euclidean or non-metric dissimilarity measures may arise when contours, spectra or shapes …

Similarity is the assessment of deviation from equivalence. Spatial similarity is complex due
to the numerous constraining properties of geographic objects and their embedding in …