Towards hybrid clustering approach to data classification: Multiple kernels based interval-valued Fuzzy C-Means algorithms

Nguyen, Dzung Dinh; Ngo, Long Thanh; Pedrycz, Witold

doi:10.1016/j.fss.2015.01.020

Cited by 40 publications

(24 citation statements)

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“…Ji et al (2014) proposed interval-valued possibilistic FCM clustering to incorporate interval type-2 sets into the possibilistic FCM to better handle and manage the uncertainty implied by data. Qiu et al (2013) introduced the modified interval type-2 FCM using spatial information to handle uncertainty in MR images. Zarandi (2014a, 2014b) proposed an algorithm of general type-2 fuzzy clustering for analyzing gene expression data with newly developed general type-2 cluster validity index.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The combination of IT2FCM and other techniques such as multiple kernel technique was also proposed (Nguyen et al, 2015). These methods handle uncertainties and deal with the input features coming from multiple sources.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They have been developed and applied to various problems (Karnik et al, 1999;Karnik and Mendel, 2001;Liang and Mendel, 2000;Mendel and John, 2002;Mendel et al, 2006;Liu, 2008;Ngo and Nguyen, 2012;Nguyen et al, 2015;Hwang and Rhee, 2007;Fisher, 2010) including data clustering. Fuzzy C-Means (FCM) clustering (Bezdek et al, 1984) and its variants have been widely applied to many problems including satellite image analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

Ngo

Mai

Pedrycz

2015

Computers & Geosciences

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

Ngo

Mai

Pedrycz

2015

Computers & Geosciences

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“…(3) The land-use types are classified by FCM with different fuzzifiers, and the results are integrated using IT2 FS [6]. Due to its ability to handle the uncertainty of membership values, the IT2FCM is widely used, and many derivative methods of IT2FCM have been developed, including the interval type-2 fuzzy possibilistic C-means (IFPCM) [7], interval-valued possibilistic fuzzy C-means (IPFCM) [8], general type-2 fuzzy C-means (GT2 FCM) [9], interval type-2 fuzzy C-means clustering with spatial information (IIT2-FCM) [10], and kernel interval-valued fuzzy C-Means (KIFCM) clustering algorithms [11]. As noted by Zarinbal et al [12], in some of these methods, the type-2 fuzzy membership functions are defuzzified into type-1 fuzzy membership functions during each iteration, and the distances between a sample and cluster centers should be expressed as singleton values when used to calculate the lower and upper membership grades in a certain class; otherwise, in these cases, some information would be lost.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Guo

Huo

2017

Remote Sensing

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Abstract:Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEI sh ) is the best choice of spectral indices for this method.

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“…Consequently, many fuzzy approaches have been proposed for brain structures segmentation such as Fuzzy C-Mean (FCM), 25 widely used for data clustering, and probably also for medical image segmentation, [26][27][28] fuzzy logic, 29 atlas-based fuzzy connectedness, [30][31][32] and a combination of these approaches. For this reason, the researchers have developed fuzzy approaches by considering different regions of the MR images as fuzzy sets.…”

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confidence: 99%

Fuzzy generalized fast marching method for 3D segmentation of brain structures

Baghdadi

Benamrane

Saïs

2017

Int J Imaging Syst Tech

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The aim of this work is to develop a new model for segmentation of brain structures in medical brain MR images. Brain segmentation is a challenging task due to the complex anatomical structure of brain structures as well as intensity nonuniformity, partial volume effects and noise. Generally the structures of interest are of relatively complicated size and have significant shape variations, the structures boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. Segmentation methods based on fuzzy models have been developed to overcome the uncertainty caused by these effects. In this study, we propose a robust and accurate brain structures segmentation method based on a combination of fuzzy model and deformable model. Our method breaks up into two great parts. Initially, a preliminary stage allows to construct the various information maps, in particular a fuzzy map, used as a principal information source, constructed using the Fuzzy C‐means method (FCM). Then, a deformable model implemented with the generalized fast marching method (GFMM), evolves toward the structure to be segmented, under the action of a normal force defined from these information maps. In this sense, we used a powerful evolution function based on a fuzzy model, adapted for brain structures. Two extensions of our general method are presented in this work. The first extension concerns the addition of an edge map to the fuzzy model and the use of some rules adapted to the segmentation process. The second extension consists of the use of several models evolving simultaneously to segment several structures. Extensive experiments are conducted on both simulated and real brain MRI datasets. Our proposed approach shows promising and achieves significant improvements with respect to several state‐of‐the‐art methods and with the three practical segmentation techniques widely used in neuroimaging studies, namely SPM, FSL, and Freesurfer.

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Towards hybrid clustering approach to data classification: Multiple kernels based interval-valued Fuzzy C-Means algorithms

Cited by 40 publications

References 40 publications

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Fuzzy generalized fast marching method for 3D segmentation of brain structures

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