Pattern classification and stratification approaches have increasingly been used in research
on Autism Spectrum Disorder (ASD) over the last ten years with the goal of translation …

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is
popular in many application fields. To assess the quality of a clustering result, different …

This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …

Across many scientific domains, there is a common need to automatically extract a simplified
view or coarse-graining of how a complex system's components interact. This general task is …

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data
mining research. Little is known regarding the strengths and weaknesses of different …

Clustering by fast search and find of Density Peaks (referred to as DPC) was introduced by
Alex Rodríguez and Alessandro Laio. The DPC algorithm is based on the idea that cluster …

Managers and researchers alike have long recognized the importance of corporate textual
risk disclosures. Yet it is a nontrivial task to discover and quantify variables of interest from …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

Existing and planned legislation stipulates various obligations to provide information about
machine learning algorithms and their functioning, often interpreted as obligations to …

Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years.
Interpretation of functional magnetic resonance imaging (fMRI) data has often occurred …