Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

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 …

Recent technological developments in high‐dimensional flow cytometry and mass cytometry
(CyTOF) have made it possible to detect expression levels of dozens of protein markers in …

Recent advances in molecular simulations have allowed scientists to investigate slower
biological processes than ever before. Together with these advances came an explosion of …

Clustering incomplete instance is still a challenging task since missing values maybe make
the cluster information ambiguous, leading to the uncertainty and imprecision in results. This …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …