Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

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

The 2nd Edition of Optical Wireless Communications: System and Channel Modelling with
MATLAB® with additional new materials, is a self-contained volume that provides a concise …

In recent years, the effect of the curse of high dimensionality has been studied in great detail
on several problems such as clustering, nearest neighbor search, and indexing. In high …

We explore the effect of dimensionality on the “nearest neighbor” problem. We show that
under a broad set of conditions (much broader than independent and identically distributed …

Clustering is a discovery process in data mining. It groups a set of data in a way that
maximizes the similarity within clusters and minimizes the similarity between two different …

Data mining applications place special requirements on clustering algorithms including: the
ability to find clusters embedded in subspaces of high dimensional data, scalability, end …

Consider a set of S of n data points in real d-dimensional space, Rd, where distances are
measured using any Minkowski metric. In nearest neighbor searching, we preprocess S into …

We propose to extend database systems by a Skyline operation. This operation filters out a
set of interesting points from a potentially large set of data points. A point is interesting if it is …

For similarity search in high-dimensional vector spaces (or 'HDVSs'), researchers have
proposed a number of new methods (or adaptations of existing methods) based, in the main …