Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

Horta, Danilo; Naldi, Murilo Coelho; Campello, Ricardo J. G. B.; Hruschka, Eduardo R.; Carvalho, André C. P. L. F. de

doi:10.1007/978-3-642-01088-0_8

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Generally; these proposed algorithms applied an optimization process (such as particle Swarms and genetic algorithm optimization) as a clustering algorithm with fitness function used for cluster validity index. For further explanation refer to (Alia et al, 2009;Das et al, 2009b;Horta et al, 2009;Hruschka et al, 2006). Alia et al (2011) in spite of the promising results that was obtained from these algorithms, a new metaheuristic algorithm must be developed tosignificantly enhance and improve the accuracy of the segmentation results.…”

Section: Ajasmentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

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Section: Ajasmentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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“…In general, these algorithms apply an optimization algorithm (such as Genetic Algorithm or Particle Swarms Optimization) as a clustering algorithm (either hard or fuzzy) with a cluster validity index as its fitness (objective) function. For further information, see [48][49][50][51] and references therein. Despite the promising results shown by these algorithms, it is desirable to develop a new metaheuristic algorithm that can improve the performance even further.…”

Section: Introductionmentioning

confidence: 99%

A hybrid harmony search algorithm for MRI brain segmentation

2011

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Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length encoding in each harmony memory vector, this algorithm is able to represent variable numbers of candidate cluster centers at each iteration. A new HS operator, called the ''empty operator'', has been introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. Evaluation of the proposed algorithm has been performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results show the ability of this algorithm to find the appropriate number of naturally occurring regions in brain images. Furthermore, the superiority of the proposed algorithm over various state-of-the-art segmentation algorithms is demonstrated quantitatively.

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“…Hybrid methods like the study of [14] have combined k-means with Genetic Algorithms GAs (a subcategory of EAs), resulting to the Incremental Genetic K-means Algorithm (IGKA), which converges to a global optimum faster than the stand alone GA and without sensitivity to the initialization of prototypes. Extensive is the research regarding the recruitment of EAs to solve the fuzzy problem (see the review of Horta et al [15]). Gong et al [16] attempted to improve the Differential Evolutionary (DE) algorithm by integrating the one-step fuzzy c-means algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

Dimitrakopoulou

Vrahatis

Wilk

et al. 2013

Computer Methods and Programs in Biomedicine

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The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web

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Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

Cited by 9 publications

References 35 publications

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

A hybrid harmony search algorithm for MRI brain segmentation

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

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