Minwise hashing is a fundamental and one of the most successful hashing algorithm in the
literature. Recent advances based on the idea of densification (Shrivastava\& Li, 2014) have …

This article reviews index structures for fast similarity search for objects represented by
binary vectors (with components equal to 0 or 1). Structures for both exact and approximate …

We present PUFFINN, a parameterless LSH-based index for solving the $ k $-nearest
neighbor problem with probabilistic guarantees. By parameterless we mean that the user is …

Minwise hashing has become a standard tool to calculate signatures which allow direct
estimation of Jaccard similarities. While very efficient algorithms already exist for the …

Introduction. Neural network models of autoassociative, distributed memory allow storage
and retrieval of many items (vectors) where the number of stored items can exceed the …

As an efficient tool for approximate similarity computation and search, Locality Sensitive
Hashing (LSH) has been widely used in many research areas including databases, data …

In this paper, we study the problem of approximate containment similarity search. Given two
records Q and X, the containment similarity between Q and X with respect to Q is| Q intersect …

The probability Jaccard similarity was recently proposed as a natural generalization of the
Jaccard similarity to measure the proximity of sets whose elements are associated with …

Abstract The Indyk-Motwani Locality-Sensitive Hashing (LSH) framework (STOC 1998) is a
general technique for constructing a data structure to answer approximate near neighbor …

Graph clustering is a fundamental data mining task that clusters vertices into different
groups. The structural graph clustering algorithm (SCAN) is a widely used graph clustering …