Identifying the queried object, from a large volume of given uncertain dataset, is a tedious
task which involves time complexity and computational complexity. To solve these …

Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved
into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT) …

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field
of pattern recognition, text categorization, object recognition etc. Its simplicity is its main …

We show an optimal data-dependent hashing scheme for the approximate near neighbor
problem. For an n-point dataset in a d-dimensional space our data structure achieves query …

In this paper, we look at improving the KD-tree for a specific usage: indexing a large number
of SIFT and other types of image descriptors. We have extended priority search, to priority …

\kdtree\citefriedman1976algorithm has long been deemed unsuitable for exact nearest-
neighbor search in high dimensional data. The theoretical guarantees and the empirical …

Granular computing is an efficient and scalable computing method. Most of the existing
granular computing-based classifiers treat the granules as a preliminary feature procession …

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

In applications, a high-dimensional data is given as a discrete set in a Euclidean space. If
the points of data are well sampled on a manifold, then the data geometry is inherited from …

A prominent approach in collaborative filtering based recommender systems is using
dimensionality reduction (matrix factorization) techniques to map users and items into low …