Clustering of 29Si NMR data of aluminosilicate glasses using, k-means and fuzzy c-means — a comparison

Ehrentreich, F.; Nofz, Marianne; Bartel, Hans-Georg

doi:10.1016/0169-7439(92)80027-2

Cited by 11 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The group membership estimated by iterative relocation clustering using a fuzzy c ‐means criterion is plotted in Figure (b). Similar to the K ‐means clustering criterion, the fuzzy c ‐means clustering criterion also tends to find circular clusters . With fuzzy c ‐means clustering, samples located in the center of two clusters tend to show low membership uncertainty, despite their positions.…”

Section: Resultsmentioning

confidence: 99%

“…Traditional clustering approaches based on agglomerative clustering or iterative relocation do not afford an easy way to assess the uncertainty of membership of samples in a cluster if not combined with resampling techniques . Clustering based on a mixture model assesses membership uncertainty by simply calculating cluster probability , whereas fuzzy c ‐means clustering, as implemented by several standard algorithms, is frequently used to obtain a fuzzy membership score, which may be identified as an “uncertainty” for samples .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian estimation of membership uncertainty in model‐based clustering

Chen

Brown

2013

Journal of Chemometrics

View full text Add to dashboard Cite

We report the use of a cluster analysis method based on a multivariate mixture model, known as model‐based clustering, for overcoming the limitations of hierarchical clustering and relocation clustering. Unlike traditional clustering methods in which clusters are formed on the basis of intercluster distances, model‐based clustering classifies observations on the basis of probability estimated from Gaussian mixture modeling, and its statistical basis allows for inference. Three examples are given in which we demonstrate that model‐based clustering gives much better performance for overlapping clusters, a more reliable determination of the number of clusters in data, and better identification of clustering in the presence of outliers than agglomerative hierarchical clustering or iterative relocation clustering using a K‐means criterion. We also show that Markov chain Monte Carlo simulation, as implemented via Gibbs sampling coupled with model‐based clustering, may be used to assess uncertainty of group memberships. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of membership uncertainty in model‐based clustering

Chen

Brown

2013

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…However, there is a confusing problem, in that the similarity index of any two isomers has two values [SI (n, m) and SI (m, n)]. For example, the similarity index [SI (5,6)] of ∆ 5 -and ∆ 6 -isomers is 77, while SI (6, 5) is 71. Obviously, SI (n, m) ≠ SI (m, n) in this fuzzy classification.…”

Section: Resultsmentioning

confidence: 99%

“…The theory of fuzzy sets provides a suitable mathematical tool for analysing complex systems. Up to now, the majority of successful applications of fuzzy set theory has been devoted to analytical chemistry, such as identification of configuration of sex pheromones using 13 C-NMR spectra, 2-3 computer-assisted multicomponent spectral analysis, 4 clustering of 29 Si-NMR data of aluminosilicate glasses, 5 and other applications. 6 We have a long-standing interest in the application of fuzzy similarity analysis to the identification of mass spectra of sex pheromones of Lepidoptera, which are mostly composed of unsaturated alcohols and acetates with chain lengths of C 12 -C 16 .…”

mentioning

confidence: 99%

Similarity between mass spectra of double-bond positional isomers of tetradecen-1-ols and their acetates

Yuan

Xiao

Wang

et al. 1997

Rapid Commun. Mass Spectrom.

View full text Add to dashboard Cite

show abstract

“…29 Si NMR data of calcium aluminosilicate glasses were clustered by both hard clustering and FCM. By optimizing the degree of fuzziness, valuable additional information on outliers could be gained by FCM results . In other research, FCM was used to cluster chemical compounds in combinatorial chemistry.…”

mentioning

confidence: 99%

Application of Fuzzy c-Means Clustering in Data Analysis of Metabolomics

Tian

Gao

et al. 2009

Anal. Chem.

View full text Add to dashboard Cite

Fuzzy c-means (FCM) clustering is an unsupervised method derived from fuzzy logic that is suitable for solving multiclass and ambiguous clustering problems. In this study, FCM clustering is applied to cluster metabolomics data. FCM is performed directly on the data matrix to generate a membership matrix which represents the degree of association the samples have with each cluster. The method is parametrized with the number of clusters (C) and the fuzziness coefficient (m), which denotes the degree of fuzziness in the algorithm. Both have been optimized by combining FCM with partial least-squares (PLS) using the membership matrix as the Y matrix in the PLS model. The quality parameters R(2)Y and Q(2) of the PLS model have been used to monitor and optimize C and m. Data of metabolic profiles from three gene types of Escherichia coli were used to demonstrate the method above. Different multivariable analysis methods have been compared. Principal component analysis failed to model the metabolite data, while partial least-squares discriminant analysis yielded results with overfitting. On the basis of the optimized parameters, the FCM was able to reveal main phenotype changes and individual characters of three gene types of E. coli. Coupled with PLS, FCM provides a powerful research tool for metabolomics with improved visualization, accurate classification, and outlier estimation.

show abstract

Clustering of 29Si NMR data of aluminosilicate glasses using, k-means and fuzzy c-means — a comparison

Cited by 11 publications

References 6 publications

Bayesian estimation of membership uncertainty in model‐based clustering

Bayesian estimation of membership uncertainty in model‐based clustering

Similarity between mass spectra of double-bond positional isomers of tetradecen-1-ols and their acetates

Application of Fuzzy c-Means Clustering in Data Analysis of Metabolomics

Contact Info

Product

Resources

About