Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Khang, Trần Đình; Vượng, Nguyễn Đức; Fowler, Michael; Fowler, Michael

doi:10.3390/a13070158

Cited by 29 publications

(14 citation statements)

References 16 publications

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“…In Fuzzy-C Means clustering, each point has a weighting associated with a particular cluster, so a point doesn't lie "in a cluster" as long as the association to the cluster is weak. The fuzzy C-means (FCM) algorithm, a method of fuzzy clustering, is an efficient algorithm for extracting rules and mining data from a dataset in which the fuzzy properties are highly common [21,22].…”

Section: Chemometrics Methodsmentioning

confidence: 99%

Mushroom’s Evaluation Based on FT-IR Fingerprint and Chemometrics

Feher¹,

Floare-Avram²,

Covaciu³

et al. 2021

Preprint

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Edible mushrooms have been recognized as highly nutritional food for a long time, due to their specific flavor, texture and also for therapeutic effects. This study proposes a new simple approach, based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushrooms species from Romanian spontaneous flora, namely Armillaria mellea, Boletus edulis and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) manage to 100% classify the three species and the cross validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for holdout set the percent raised at 94.74%. The lowered values obtained for the training set was due to one C. cibarius sample, two B. edulis and five A. mellea, which were placed to other species. Anyway, for holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified investigated mushroom samples according to their species, meaning that in every partition the predominant specie had the biggest DOMs, while samples belonging to other specie had lower DOMs.

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Section: Chemometrics Methodsmentioning

confidence: 99%

Mushroom’s Evaluation Based on FT-IR Fingerprint and Chemometrics

Feher¹,

Floare-Avram²,

Covaciu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In fuzzy c-means clustering, each point has a weighting associated with a particular cluster, so a point does not lie "in a cluster" as long as the association to the cluster is weak. The fuzzy c-means algorithm, a method of fuzzy clustering, is an efficient algorithm for extracting rules and mining data from a dataset in which the fuzzy properties are highly common [21,22]. For this study, the main purpose of using c-means clustering is the partition of experimental datasets into a collection of clusters (mushrooms species), where, for each data point, a membership value is assigned for each class.…”

Section: Chemometrics Methodsmentioning

confidence: 99%

Evaluation of Mushrooms Based on FT-IR Fingerprint and Chemometrics

et al. 2021

View full text Add to dashboard Cite

Edible mushrooms have been recognized as a highly nutritional food for a long time, thanks to their specific flavor and texture, as well as their therapeutic effects. This study proposes a new, simple approach based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100% of the three species, and the cross-validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on the B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for the holdout set, the percent rose to 94.74%. The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and five A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples according to their species, meaning that, in every partition, the predominant species had the biggest DOMs, while samples belonging to other species had lower DOMs.

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“…In FCM, the parameter ‘degree of fuzzification’, is selected as greater than 1. There is no specific rule to select its value (Khang et al, 2020). The value 1 is used for hard clustering and a value greater than 1 is used to add the fuzziness.…”

Section: Post‐processingmentioning

confidence: 99%

“…In FCM, the parameter 'degree of fuzzification', is selected as greater than 1. There is no specific rule to select its value (Khang et al, 2020).…”

Section: Fuzzy C-meansmentioning

confidence: 99%

Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

et al. 2021

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The segmentation of breast lesions is an important step in the computer-aided analysis of the mammogram. The presence of noise in mammograms makes lesion detection challenging particularly for complex malignant lesions. Pre-processing techniques can deal with the noise issue but distorts the important shape features. This motivates us to propose a novel hybrid approach by combining a convolution neural network (CNN) with connected component analysis (CCA) to segment malignant breast lesions without any pre-processing to avoid any distortion in image sharpness at the initial stages. Two well-known segmentation techniques namely, K-means (KM) and Fuzzy c-means (FCM) are also used to compare the results. From a pool of 1045 mammographic cancer images acquired from the Digital Database for Screening Mammography (DDSM), 1016 are used for training and validation, and 29 are used for testing. All three results (Hybrid, KM and FCM) are compared against the results by the expert Radiologist. The results indicate that, among various segmentation techniques, the proposed hybrid approach achieves the highest accuracy (90%), Matthew's correlation coefficient (0.79), Jaccard index (0.73) and the Dice similarity coefficient (0.84). Other performance evaluation techniques such as; precision, sensitivity, specificity, false-positive rate, false discovery rate, negative predictive value and false-negative rate also show the superior performance of the proposed hybrid approach. Statistical analysis (Mann-Whitney U test, T-test, Chi-square test, Kolmogorov-Smirnov test and Wilcoxon test), graphical analysis (Regression and Bland-Altman plots) and receiver operating characteristic curve further demonstrate the stability and consistency of the results.

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Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Cited by 29 publications

References 16 publications

Mushroom’s Evaluation Based on FT-IR Fingerprint and Chemometrics

Mushroom’s Evaluation Based on FT-IR Fingerprint and Chemometrics

Evaluation of Mushrooms Based on FT-IR Fingerprint and Chemometrics

Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

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