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 paper. 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 multiobjective 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 nonoriented 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 are 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.

/pdf/a-survey-of-evolutionary-algorithms-for-clustering-2mqgdycpl7.pdf

A Survey of Evolutionary Algorithms for Clustering

/pdf/tecnicas-de-combinacao-para-agrupamento-centralizado-e-2fitekxwph.pdf

Técnicas de combinação para agrupamento centralizado e distribuído de dados

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii List of Abbreviations Used . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv Chapter

Symbiotic Evolutionary Subspace Clustering (S-ESC)

 This paper presents EDACluster, an Estimation of Distribution Algorithm (EDA) applied to the clustering task EDA is an Evolutionary Algorithm used here to optimize the search for adequate clusters when very little is known about the target dataset The proposed algorithm uses a mixed approach – density and grid-based – to identify sets of dense cells in the dataset The output is a list of items and their associated clusters Items in low-density areas are considered noise and are not assigned to any cluster This work uses four public domain datasets to perform the tests that compare EDACluster with DBSCAN, a conventional density-based clustering algorithm 1 Introduction The practice of classifying objects based on observed similarities is noticed in several daily tasks and is a common practice in different fields Cluster analysis is the formal study of algorithms and methods to group, or to classify objects The goal of a clustering algorithm is to find a convenient and valid organization for unexplored data (Jain et al, 1999) In other words, cluster analysis is a way to organize objects in clusters based on similarity Intuitively, objects that belong to a group are more similar to each other than they are to objects from different groups Besides the applications in computer science, as the exploratory data analysis, data mining, image processing and pattern recognition, cluster analysis has a vast list of applications in Biology, Psychology, Archeology, Geology, Geography and Marketing (Jain and Dubes, 1988)

An Evolutionary Density and Grid-Based Clustering Algorithm

This paper presents EDACluster, an Estimation of Distribution Algorithm (EDA) applied to the clustering task. EDA is an Evolutionary Algorithm used here to optimize the search for adequate clusters when very little is known about the target dataset. The proposed algorithm uses a mixed approach – density and grid-based – to identify sets of dense cells in the dataset. The output is a list of items and their associated clusters. Items in low-density areas are considered noise and are not assigned to any cluster. This work uses four public domain datasets to perform the tests that compare EDACluster with DBSCAN, a conventional density-based clustering algorithm.

EDACluster: An Evolucionary Density and Grid-Based Clustering Algorithm.

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An Evolutionary Density and Grid-Based Clustering Algorithm

EDACluster: An Evolucionary Density and Grid-Based Clustering Algorithm.