Parallelizing data clustering algorithms has attracted the interest of many researchers over
the past few years. Many efficient parallel algorithms were proposed to build partitioning …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

We analyze a compression scheme for large data sets that randomly keeps a small
percentage of the components of each data sample. The benefit is that the output is a sparse …

Data generation has increased drastically over the past few years. Data management has
also grown in importance because extracting the significant value out of a huge pile of raw …

The immense amount of daily generated and communicated data presents unique
challenges in their processing. Clustering, the grou** of data without the presence of …

Clustering a large volume of data in a distributed environment is a challenging issue. Data
stored across multiple machines are huge in size, and solution space is large. Genetic …

Data clustering is a valuable field for extracting effective information and hidden patterns
from datasets. In this paper we propose a clustering approach based on density peaks …

The Nystrom method is a popular technique for generating low-rank approximations of
kernel matrices that arise in many machine learning problems. The approximation quality of …

Big data analytics gain significant interest over the traditional data‐processing
methodologies that engage in extracting the hidden patterns and correlations from the …

Analyzing the multi-dimensional data in faster way is an important and basic aspect in any
clustering mechanism. At the same time the clustering mechanism should provide a security …