A Fuzzy Variant of an Evolutionary Algorithm for Clustering

Alves, Vinícius S.; Campello, Ricardo J. G. B.; Hruschka, Eduardo R.

doi:10.1109/fuzzy.2007.4295395

Cited by 7 publications

(10 citation statements)

References 14 publications

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“…Alternatively, the reader may think about using conventional clustering algorithms for fixed k, such as k-means [101][72], EM (Expectation Maximization) [34] [61], and SOM (Self-Organized Maps) [17][62] algorithms. However, these prototype-based algorithms are quite sensitive to initialization of prototypes 1 and may get stuck at sub-optimal solutions. This is a wellknown problem, which becomes more evident for more complex data sets 2 .…”

Section: A Algorithms With Fixed Number Of Clustersmentioning

confidence: 99%

“…Roughly speaking, more fitted partitions have higher probabilities of being sampled. Thus, the evolutionary search is biased towards more promising clustering solutions and tends to perform a more 1 We here define a prototype as a particular feature vector that represents a given cluster. For instance, prototypes can be centroids, medoids, or any other vector computed from the data partition and that represents a cluster (as in the case of typical fuzzy clustering algorithms).…”

Section: A Algorithms With Fixed Number Of Clustersmentioning

confidence: 99%

“…More precisely, the authors propose to minimize the sum of distances between the objects of the data set X = {x 1 , x 2 , …, x N } and the medoids from the set {m 1 …”

Section: To Appear In Ieee Transactions On Systems Man and Cybernetmentioning

confidence: 99%

“…This criterion is essentially the well-known sum of within-cluster distances applied to a medoid-based representation. The genetic algorithms developed by EstivillCastro and Murray [39] and Sheng and Liu [122] are also aimed at minimizing F in Equation (1). Some authors (Murthy and Chowdhury [107]; Maulik and Bandyopadhyay [100]; Bandyopadhyay and Maulik [10]; Merz and Zell [103]) propose to minimize the sum of squared Euclidean distances of the objects from their respective cluster means.…”

Section: To Appear In Ieee Transactions On Systems Man and Cybernetmentioning

confidence: 99%

“…The second category of evolutionary fuzzy clustering algorithms is composed of algorithms that use some variant of FCM as a local search operator to speed up their convergence by refining rough partitions explored by the evolutionary search, while providing the necessary computations to get the fuzzy partition [1][19] [67]. In [19] it is shown that an evolutionary algorithm is able to outperform, in terms of computational efficiency, traditional approaches to determine satisfactory estimates of the unknown number of fuzzy clusters, under both the theoretical (asymptotic time complexity analyses) and experimental (statistical analyses) perspectives.…”

Section: A Fuzzy Clusteringmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Evolutionary Algorithms for Clustering

Hruschka

Campello

Freitas

et al. 2009

IEEE Trans. Syst., Man, Cybern. C

663

316

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-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.

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Section: A Algorithms With Fixed Number Of Clustersmentioning

confidence: 99%

Section: A Algorithms With Fixed Number Of Clustersmentioning

confidence: 99%

“…More precisely, the authors propose to minimize the sum of distances between the objects of the data set X = {x 1 , x 2 , …, x N } and the medoids from the set {m 1 …”

Section: To Appear In Ieee Transactions On Systems Man and Cybernetmentioning

confidence: 99%

Section: To Appear In Ieee Transactions On Systems Man and Cybernetmentioning

confidence: 99%

Section: A Fuzzy Clusteringmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Evolutionary Algorithms for Clustering

Hruschka

Campello

Freitas

et al. 2009

IEEE Trans. Syst., Man, Cybern. C

663

316

View full text Add to dashboard Cite

show abstract

Genetic algorithms for clustering and fuzzy clustering

Bandyopadhyay¹

2011

WIREs Data Min & Knowl

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Clustering has been an area of intensive research for several decades because of its multifaceted applications in innumerable domains. Clustering can be either Boolean, where a single data point belongs to exactly one cluster, or fuzzy, where a single data point can have nonzero belongingness to more than one cluster. Traditionally, optimization of some well-defined objective function has been the standard approach in both clustering and fuzzy clustering. Hence, researchers have investigated the utility of evolutionary computing and related techniques in this regard. The different approaches differ in their choice of the objective function and/or the optimization strategy used. In particular, clustering using genetic algorithms (GAs) has attracted attention of researchers, and has been studied extensively. This paper presents a short review of some of different approaches of GA-based clustering methods. Two techniques, one with fixed number of clusters and another with a variable number of fuzzy clusters, are described along with some experimental results on numerical as well as image data sets.

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