Core decomposition in networks has proven useful for evaluating the importance of nodes
and communities in a variety of application domains, ranging from biology to social networks …

This paper presents a new clustering algorithm for space–time data based on the concepts
of topological data analysis and, in particular, persistent homology. Employing persistent …

During the last decade, substantial resources have been invested to exploit massive
amounts of boreholes data collected through groundwater extraction. Furthermore …

Blockchains are significantly easing trade finance, with billions of dollars worth of assets
being transacted daily. However, analyzing these networks remains challenging due to the …

A new depth-based clustering procedure for directional data is proposed. Such method is
fully non-parametric and has the advantages to be flexible and applicable even in high …

The contour depth methodology enables non‐parametric summarization of contour
ensembles by extracting their representatives, confidence bands, and outliers for …

We develop a new density-based clustering algorithm named CRAD which is based on a
new neighbor searching function with a robust data depth as the dissimilarity measure. Our …

Angle-based outlier detection (ABOD) has been recently emerged as an effective method to
detect outliers in high dimensions. Instead of examining neighborhoods as proximity-based …

With the recent progress of data acquisition technology, classification of data exhibiting
relational dependence, from online social interactions to multi-omics studies to linkage of …

This study provides an automatic ship**-route construction method using functional data
analysis (FDA), which analyzes information about curves, such as multiple data points over …