On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series

Fadili, M.J.; Ruan, Su; Bloyet, Daniel; Mazoyer, Bernard

doi:10.1016/s1361-8415(00)00035-9

Cited by 69 publications

(58 citation statements)

References 29 publications

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“…Pal and Bezdek (1995) have given heuristic guidelines regarding the best choice for m, suggesting that the value of the level of fuzziness should be between 1.5 and 2.5. Similar recommendations appear in Cannon et al (1986), Hall et al (1992), Soreson and Wang (1996) and Fadili et al (2001).…”

supporting

confidence: 54%

A fuzzy taxonomy for e-Health projects

D’Urso

Giovanni

Spagnoletti

2012

Int. J. Mach. Learn. & Cyber.

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Abstract. Evaluating the impact of Information Technology (IT) projects represents a problematic task for policy and decision makers aiming to define roadmaps based on previous experiences. Especially in the healthcare sector IT can support a wide range of processes and it is difficult to analyze in a comparative way the benefits and results of e-Health practices in order to define strategies and to assign priorities to potential investments. A first step towards the definition of an evaluation framework to compare e-Health initiatives consists in the definition of clusters of homogeneous projects that can be further analyzed through multiple case studies. However imprecision and subjectivity affect the classification of e-Health projects that are focused on multiple aspects of the complex healthcare system scenario. In this paper we apply a method, based on advanced cluster techniques and fuzzy theories, for validating a project taxonomy in the e-Health sector. An empirical test of the method has been performed over a set of European good practices in order to define a taxonomy for classifying e-Health projects.

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supporting

confidence: 54%

A fuzzy taxonomy for e-Health projects

D’Urso

Giovanni

Spagnoletti

2012

Int. J. Mach. Learn. & Cyber.

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“…2. Fuzzy clustering: FCM clustering is applied to the features obtained in step 1, with the number of clusters obtained from the method proposed in Fadili et al (18). After FCM converges, the cluster with the centroid most similar to the activation pattern is chosen as the active cluster.…”

Section: Discussionmentioning

confidence: 99%

“…A key problem in clustering is determination of the optimal number of clusters. To this end, we use the cluster validity measure proposed by Fadili et al (18). We also use the fuzziness index m ϭ 2 as suggested (18).…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

“…To this end, we use the cluster validity measure proposed by Fadili et al (18). We also use the fuzziness index m ϭ 2 as suggested (18). After FCM converges, the cluster with the centroid most similar to the reference pattern is chosen as the active cluster.…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

“…In using FCM for fMRI activation detection, one faces unresolved issues such as predefinition of the number of clusters (cluster validation), statistical analysis of the results, and their instability when applied to the raw fMRI time series. To define the optimal number of clusters, usually a cluster validity measure, such as the cluster validity measure proposed by Fadili et al (18), is used. However, intensive search for a widely accepted index to determine the number of clusters has not yet succeeded (19).…”

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confidence: 99%

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Functional magnetic resonance imaging activation detection: Fuzzy cluster analysis in wavelet and multiwavelet domains

Jahanian

Soltanian-Zadeh

Hossein-Zadeh

2005

Magnetic Resonance Imaging

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Purpose: To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Materials and Methods:Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. Results:The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. Conclusion:More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.

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Methods for detecting functional classifications in neuroimaging data

Bowman

Patel

2004

Human Brain Mapping

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Data-driven statistical methods are useful for examining the spatial organization of human brain function. Cluster analysis is one approach that aims to identify spatial classifications of temporal brain activity profiles. Numerous clustering algorithms are available, and no one method is optimal for all areas of application because an algorithm's performance depends on specific characteristics of the data. K-means and fuzzy clustering are popular for neuroimaging analyses, and select hierarchical procedures also appear in the literature. It is unclear which clustering methods perform best for neuroimaging data. We conduct a simulation study, based on PET neuroimaging data, to evaluate the performances of several clustering algorithms, including a new procedure that builds on the kth nearest neighbor method. We also examine three stopping rules that assist in determining the optimal number of clusters. Five hierarchical clustering algorithms perform best in our study, some of which are new to neuroimaging analyses, with Ward's and the beta-flexible methods exhibiting the strongest performances. Furthermore, Ward's and the beta-flexible methods yield the best performances for noisy data, and the popular K-means and fuzzy clustering procedures also perform reasonably well. The stopping rules also exhibit good performances for the top five clustering algorithms, and the pseudo-T2 and pseudo-F stopping rules are superior for noisy data. Based on our simulations for both noisy and unscaled PET neuroimaging data, we recommend the combined use of the pseudo-F or pseudo-T2 stopping rule along with either Ward's or the beta-flexible clustering algorithm.

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On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series

Abstract: . On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series.

Cited by 69 publications

References 29 publications

A fuzzy taxonomy for e-Health projects

A fuzzy taxonomy for e-Health projects

Functional magnetic resonance imaging activation detection: Fuzzy cluster analysis in wavelet and multiwavelet domains

Methods for detecting functional classifications in neuroimaging data

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