Space partitions of $\mathbb {R}^ d $ underlie a vast and important class of fast nearest
neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general …

This paper studies density-based clustering of point sets. These methods use dense regions
of points to detect clusters of arbitrary shapes. In particular, we study variants of density …

This paper provides a review and comparative analysis of trajectory outlier detection
methods. It presents the definition of outliers in trajectory data and the existing types to …

Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing
attention in academia and industry. Given a set of data objects, each being a pair of a high …

Data lakes store diverse and large volumes of datasets. One of the core challenges in data
lakes is dataset discovery, which involves tasks such as finding related tables, domain …

Graph-based indexes have been widely employed to accelerate approximate similarity
search of high-dimensional vectors. However, the performance of graph indexes to answer …

We define and investigate the problem of $\textit {c-approximate window search} $:
approximate nearest neighbor search where each point in the dataset has a numeric label …

We propose a new" bi-metric" framework for designing nearest neighbor data structures. Our
framework assumes two dissimilarity functions: a ground-truth metric that is accurate but …

We present a theoretical and empirical analysis of the adaptive entry point selection for
graph-based approximate nearest neighbor search (ANNS). We introduce novel concepts …

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal
challenge in the field of machine learning. In recent years, graph-based methods have …