… clustering algorithms. This paper presents an overview of the latest parallel clustering
algorithms categorized according to the computing platforms used to handle the Big Data, namely, …

… sets of eleven clustering algorithms. This paper analyses how these algorithms behave
with five different multivariate data sets in data representation. To answer this question, we …

… This paper presents a review of previous research related to artificial bee colony algorithm,
ABC variants and applications in data clustering. ABC is a simple and flexible algorithm and …

… The chosen algorithms in this study are partitioning and hierarchical user-dependent
algorithms, where, the datasets on hand are clustered data and the number of clusters is known. …

… theoretical view on clustering algorithms especially for healthcare data analysis from both …
clustering algorithms deliberated in the existing studies for analyzing healthcare data sets and …

… Data clustering Data mining Optimisation based-clustering … the M-ACOC algorithm to solve
data clustering problems has … are most commonly used in the clustering evaluation domain. …

In this paper, a new clustering algorithm inspired by magnetic force is proposed. This algorithm
is not sensitive to the initialization problem of cluster centroids. Centroid particles change …

… The clustering algorithms come with their own set of chal… of data and the mechanism
adopted to form clusters, we mention certain drawbacks incurred by various clustering algorithms: …

… Several clustering algorithms based on Genetic Algorithms … in partitioning data with that
of genetic algorithms of performing … In this paper, a genetic algorithm that integrates the local …

… K-means algorithm is used to minimize … algorithm, the number of clusters present in the
data need to be known; multiple runs or trials will be necessary to find the best number of clusters…