-means (km) is a clustering algorithm that has been widely adopted due to its simple
implementation and high clustering quality. However, the standard km suffers from high …

The K-Means algorithm for cluster analysis is one of the most influential and popular data
mining methods. Its straightforward parallel formulation is well suited for distributed memory …

We propose a dynamically load-balanced algorithm for parallel particle tracing, which
periodically attempts to evenly redistribute particles across processes based on kd tree …

The standard formulation of the K-means clustering (Lloyd's method) performs many
unnecessary distance calculations. In this paper, we focus on four approaches that use the …

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 …

Searching is one of the most fundamental operations in many complex systems. However,
the complexity of the search process would increase dramatically in high‐dimensional …

Cloud computing is an elastic computing model that the users can lease the resources from
the rentable infrastructure. Cloud computing is gaining popularity due to its lower cost, high …

Delaunay tessellations are fundamental data structures in computational geometry. They are
important in data analysis, where they can represent the geometry of a point set or …

This paper considers the problem of exact accelerated algorithms for the K-means clustering
of low-dimensional data on modern multi-core systems. A version of the filtering algorithm …

DBSCAN is one of the most popular density-based clustering algorithm capable of
identifying arbitrary shaped clusters and noise. It is computationally expensive for large data …