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

During the last decade, multimedia databases have become increasingly important in many
application areas such as medicine, CAD, geography, and molecular biology. An important …

Information retrieval (IR) has changed considerably in recent years with the expansion of the
World Wide Web and the advent of modern and inexpensive graphical user interfaces and …

The nearest-or near-neighbor query problems arise in a large variety of database
applications, usually in the context of similarity searching. Of late, there has been increasing …

Presents a review of 200 references in content-based image retrieval. The paper starts with
discussing the working conditions of content-based retrieval: patterns of use, types of …

In this paper we develop a new data structure for implementing heaps (priority queues). Our
structure, Fibonacci heaps (abbreviated F-heaps), extends the binomial queues proposed …

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 …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

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 …