In this work we propose a hierarchical clustering methodology for hyperspectral data based on the Hotelling’s T 2 statistic. For each hypespectral sample data, the statistical sample mean is calculated using a window-based neighborhood. Then, the pairwise similarities between any two hyperspectral samples are computed in base to the Hotelling’s T 2 statistic. This statistic assumes a Gaussian d...

We study a one-dimensional model of gravitational instability in an Einstein-de Sitter universe. Scaling in both space and time results in an autonomous set of coupled Poisson-Vlasov equations for both the field and phase space density, and the N-body problem. Using dynamical simulation, we find direct evidence of hierarchical clustering. A multifractal analysis reveals a bifractal geometry sim...

The problem of hierarchical clustering items from pairwise similarities is found across various scientific disciplines, from biology to networking. Often, applications of clustering techniques are limited by the cost of obtaining similarities between pairs of items. While prior work has been developed to reconstruct clustering using a significantly reduced set of pairwise similarities via adapt...

Our paper is concerned with investigating the impact of translationese on the novels of a bilingual writer and asking whether one could determine the authorship of a translated document. The main part of our paper will be centered on selecting a good set of lexical features that can be considered characteristic for an author. We used in our research the novels of Vladimir Nabokov, a bilingual a...

We present a new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior. Unlike existing approaches, our algo rithm uses an ensemble of statistical and heuristic classifiers to predict locations. We find that a hierarchical classifica tion approach can improve predic...

Clustering algorithms that output a hierarchical dendrogram as return are classified as hierarchical clustering algorithms. The most desirable feature of the hierarchical clustering algorithm is that a hierarchical dendrogram is generated. This feature is very important for applications such as in biological, social, and behavior studies, due to the need to construct taxonomies. One general pro...

In the election of a hierarchical clustering method, theoretic properties may give some insight to determine which method is the most suitable to treat a clustering problem. Herein, we study some basic properties of two hierarchical clustering methods: α-unchaining single linkage or SL(α) and a modified version of this one, SL∗(α). We compare the results with the properties satisfied by the cla...