From evolution to swarm intelligence : data clustering survey

Abstract EN

The clustering algorithms have evolved over the last decade.

With the continuous success of natural inspired algorithms in solving many engineering problems, it is imperative to scrutinize the success of these methods applied to data mining.

This paper provides a survey for the data clustering algorithms as of applications of data mining form evolutionary and swarm intelligence perspective.

In this paper, We will familiarize the reader with the different evolutionary and swarm intelligence techniques effortlessly.

The clustering techniques that will be covered in this paper are differential evolution (DE), genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO).

Moreover, an overview of the main characteristics of the clustering algorithms presented in a comparative way.