Introduction to Evolutionary Data Clustering and Its Applications

Clustering is concerned with splitting a dataset into groups (clusters) that represent the natural homogeneous characteristics of the data. Remarkably, clustering has a crucial role in numerous types of applications. Essentially, the applications include social sciences, biological and medical applications, information retrieval and web search algorithms, pattern recognition, image processing, machine learning, and data mining. Even that clustering is ubiquitous over a variety of areas. However, clustering approaches suffer from several drawbacks. Mainly, they are highly susceptible to clusters’ initial centroids which allows a particular dataset to easily fall within a local optimum. Handling clustering as an optimization problem is deemed an NP-hard optimization problem. However, metaheuristic algorithms are a dominant class of algorithms for solving tough and NP-hard optimization problems. This chapter anticipates the use of evolutionary algorithms for addressing the problem of clustering optimization. Therefore, it presents an introduction to clustering and evolutionary data clustering, reviews thoroughly the applications of evolutionary data clustering and its implementation approaches.