The k-medoids (k-median) problem is one of the best known unsupervised clustering
problems. Due to its complexity, finding high-quality solutions for huge-scale datasets …

In this paper, we propose a novel implementation for solving the large-scale k-medoids
clustering problem. Conversely to the most famous k-means, k-medoids suffers from a …

Abstract The advent of Big Data has led to the rapid growth in the usage of parallel
clustering algorithms that work over distributed computing frameworks such as MPI …

The possibility of clustering objects represented by structured data with possibly non-trivial
geometry certainly is an interesting task in pattern recognition. Moreover, in the Big Data era …

Facility location problems form a broad class of optimization problems extremely popular in
combinatorial optimization and operations research. In every facility location problem, one …

Long-reach passive optical networks (LR-PONs) prove to be a suitable candidate for future
broadband access networks. The longer reach of the feeder fiber in a LR-PON enables us to …

Abstract k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses
pairwise distances of data points and tries to directly decompose the dataset with n points …

При построении блочных шифров в качестве S-блоков необходимо использовать
векторные булевы функции со специальными криптографическими свойствами для …

DBSCAN clustering analyses can be improved by pre-processing of a data set using a
Hilbert curve to intelligently identify the centers for initial partitional analysis by a partitional …

Most commonly used clustering algorithms are those aimed at solving the well-known k-
median problem. Their main advantage is that they are simple to implement and use, and …