A Survey of Evolutionary Algorithms for Clustering

Abstract

-This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the manuscript. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics, like multi-objective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or non-oriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer or real encodings, centroid-based, medoid-based, label-based, tree-based or graph-based representations, among others. A number of references is provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

A Genetic Algorithm to Find the Adequate Granularity for Service Interfaces

“…To represent the chromosomes we use a label-based integer encoding widely adopted in literature [11] and shown in Figure 5. According to this encoding, a solution is an integer array of n positions, where n is the number of methods exposed in a service.…”

“…As crossover operator we use the operator defined specifically for clustering problems by Hruschka et al [11]. In order to illustrate how this operator works consider the example shown in Figure 6 from [11]. The operator first selects randomly k (1≤k≤n) clusters from ParentA, where n is the number of clusters in ParentA.…”

“…This step is necessary to ensure genetic diversity from one generation to the next ones. The mutation is performed selecting one of the following cluster-oriented mutation operators (randomly selected) [5], [11]:…”

Center of Gravity PSO for Partitioning Clustering

“…Clustering algorithms can be categorized as hierarchical or partitioning [2]. Hierarchical clustering algorithms group objects in a hierarchical structure.…”

OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

“…Further progress was observed when many standard clustering algorithms in gene expression analysis, such as k-means and fuzzy c-means, were coupled with Evolutionary Algorithms (EAs) [12][13] to optimize partitioning. EAs are metaheuristics widely believed to be effective on NP-hard problems, such as clustering, being able to provide near-optimal solutions in reasonable time.…”