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 …

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 …

In this article, we present an efficient B+-tree based indexing method, called iDistance, for K-
nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the …

The performance of similarity measures for search, indexing, and data mining applications
tends to degrade rapidly as the dimensionality of the data increases. The effects of the so …

Modern On-Line Data Intensive (OLDI) applications have evolved from monolithic systems to
instead comprise numerous, distributed microservices interacting via Remote Procedure …

Nearest neighbor search and many other numerical data analysis tools most often rely on
the use of the euclidean distance. When data are high dimensional, however, the euclidean …

In this paper, we present an efficient method, called iDistance, for K-nearest neighbor (KNN)
search in a high-dimensional space. iDistance partitions the data and selects a reference …

Nearest neighbor (NN) search in high dimensional space is an important problem in many
applications. Ideally, a practical solution (i) should be implementable in a relational …

Spatial queries in high-dimensional spaces have been studied extensively. Among them,
nearest neighbor queries are important in many settings, including spatial databases (Find …