Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena whic...

For learning purposes, representations of real world objects can be built by using the concept of dissimilarity. In such a case, an object is characterized in a relative way, i.e. by its dissimilarities to a set of the selected prototypes. Such dissimilarity representations are found to be more practical for some pattern recognition problems. When experts cannot decide for a single dissimilarit...

Sample- and gene-based hierarchical cluster analyses have been widely adopted as tools for exploring gene expression data in high-throughput experiments. Gene expression values (read counts) generated by RNA sequencing technology (RNA-seq) are discrete variables with special statistical properties, such as over-dispersion and right-skewness. Additionally, read counts are subject to technology a...

We study a dissimilarity measure between shapes, expressed by the natural pseudodistance between size pairs, where a shape is viewed as a topological space endowed with a real-valued continuous function. Measuring dissimilarity amounts to minimizing the change in the functions due to the application of homeomorphisms between topological spaces, with respect to the L∞-norm. A new class of shape ...

The obtention of a set of homogeneous classes of pages according to the browsing patterns identified in web server log files can be very useful for the analysis of organization of the site and of its adequacy to user needs. Such a set of homogeneous classes is often obtained from a dissimilarity measure between the visited pages defined via the visits extracted from the logs. There are however ...

IcePick is a system for computationally selecting diverse sets of molecules. It computes the dissimilarity of the surface-accessible features of two molecules, taking into account conformational flexibility. Then, the intrinsic diversity of an entire set of molecules is calculated from a spanning tree over the pairwise dissimilarities. IcePick's dissimilarity measure is compared against traditi...

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Her...

In machine learning the Fuzzy c-Means algorithm (FCM) plays an important role. This prototype based unsupervised clustering method has been extensively studied and applied to a great variety of problems from different research areas like medicine and biology. Commonly the Euclidean distance is used as dissimilarity measure, although any dissimilarity measure would be suited. Recently divergence...

Interval data is attracting attention from the data analysis community due to its ability to describe complex concepts. Since clustering is an important data analysis tool, extending these techniques to interval data is important. Applying traditional clustering methods on interval data loses information inherited in this particular data type. This paper proposes a novel dissimilarity measure w...