Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

Rapidly increasing quality of AI-generated content makes it difficult to distinguish between
human and AI-generated texts, which may lead to undesirable consequences for society …

Dealing with uncertainty in applications of machine learning to real-life data critically
depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been …

Evaluating the performance of a learning algorithm is one of the basic tasks in machine
learning and data science. In this chapter, we review commonly used performance …

Modern datasets are characterized by numerous features related by complex dependency
structures. To deal with these data, dimensionality reduction techniques are essential. Many …

Mitigating the risk arising from extreme events is a fundamental goal with many applications,
such as the modelling of natural disasters, financial crashes, epidemics, and many others …

Abstract Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many
data mining and machine learning tasks, including dimensionality reduction, outlier …

Most of the existing methods for estimating the local intrinsic dimension of a data distribution
do not scale well to high dimensional data. Many of them rely on a non-parametric nearest …

Classifier explanations have been identified as a crucial component of knowledge
discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given …

A long-standing challenge in the design of single atom alloys (SAAs), for catalytic
applications, is the determination of a feature space that maximally correlates to molecular …