The k-means clustering algorithm is considered one of the most powerful and popular data
mining algorithms in the research community. However, despite its popularity, the algorithm …

In recent years, spectral clustering has become one of the most popular modern clustering
algorithms. It is simple to implement, can be solved efficiently by standard linear algebra …

Information theoretic based measures form a fundamental class of similarity measures for
comparing clusterings, beside the class of pair-counting based and set-matching based …

AMS :: Bulletin of the American Mathematical Society Skip to Main Content American
Mathematical Society American Mathematical Society MathSciNet Bookstore Publications …

Estimation of structure, such as in variable selection, graphical modelling or cluster analysis,
is notoriously difficult, especially for high dimensional data. We introduce stability selection …

Learning problems form an important category of computational tasks that generalizes many
of the computations researchers apply to large real-life data sets. We ask, What concept …

A challenging problem in estimating high-dimensional graphical models is to choose the
regularization parameter in a data-dependent way. The standard techniques include $ K …

Animal acoustic communication often takes the form of complex sequences, made up of
multiple distinct acoustic units. Apart from the well‐known example of birdsong, other …

Abstract Constructivist philosophy and Hasok Chang's active scientific realism are used to
argue that the idea of “truth” in cluster analysis depends on the context and the clustering …

A popular method for selecting the number of clusters is based on stability arguments: one
chooses the number of clusters such that the corresponding clustering results are" most …