Centroids are key components in many data analysis algorithms such as clustering or
microaggregation. They are considered as the central value that minimises the distance to …

Given a scattering of observations on a map it is natural for one to want to determine the
most likely origin of those points, and the origin is typically hidden within data. Using an …

Pseudo-centroid clustering replaces the traditional concept of a centroid expressed as a
center of gravity with the notion of a pseudo-centroid (or a coordinate free centroid) which …

In this paper, we present the second generation of the nomclust R package, which we
developed for the hierarchical clustering of data containing nominal variables (nominal …

Abstract'symbolic Data Analysis'(SDA) provides tools for analyzing'symbolic'data, ie, data
matrices X=(x kj) where the entries x kj are intervals, sets of categories, or frequency …

We design coresets for Ordered k-Median, a generalization of classical clustering problems
such as k-Median and k-Center. Its objective function is defined via the Ordered Weighted …

The ability to cluster high-dimensional categorical data is essential for many machine
learning applications such as bioinfomatics. Currently, central clustering of categorical data …

In this paper, we discuss methods of measuring the performance of ontology-based
information extraction systems. We focus particularly on the Balanced Distance Metric …

A methodological and computational framework for centroid-based partitioning cluster
analysis using arbitrary distance or similarity measures is presented. The power of high …

Despite recent efforts, the challenge in clustering categorical and mixed data in the context
of big data still remains due to the lack of inherently meaningful measure of similarity …