Principal component analysis versus fuzzy principal component analysisA case study: the quality of danube water (1985–1996)

Sârbu, Costel; Pop, Horia F.

doi:10.1016/j.talanta.2004.08.047

Cited by 159 publications

(101 citation statements)

References 21 publications

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“…Several contributions have been proposed for Principal Component Analysis (PCA) in a fuzzy domain, e.g., Yabuuch and Watada (1997), Yang and Wang (2000), Sarbu and Pop (2005) Xian et al (2013), and Baklouti et al (2016).…”

Section: Principal Component Analysis For Imprecise Datamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

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Section: Principal Component Analysis For Imprecise Datamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

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“…PCA technique was used to develop principle components that will account for most of the variance in the observed variables which were then used as predictor or criterion variables in subsequent analyses (McKenzie, 2003). The PCA is a multivariate statistical technique used to reduce the number of variables without losing too much information in the process (Sarbu and Pop, 2005). The PCA technique achieves this by creating a fewer number of variables which explain most of the variation in the original variables (Giri, 2004).…”

Section: Construction Of Vulnerability Indicesmentioning

confidence: 99%

Assessing rural farmers perceptions and vulnerability to climate change in uMzinyathi District of Kwazulu-Natal, South Africa

Shisanya¹,

Mafongoya²

2017

Afr. J. Agric. Res.

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There is little awareness among rural farming communities on their vulnerability to climate change. This paper examined the vulnerabilities of the rural small scale farming communities in the uMzinyathi District of KwaZulu-Natal, South Africa to climate change. A survey among 200 households who were randomly chosen but who had lived in the community for over twenty years was conducted. Focus group discussions and key informant interviews were carried out to obtain qualitative data. Over the period 1993 to 2010, average annual temperature had increased by 1.5°C. Rainfall generally decreased over the period 1981 to 2010 with a range of 907 mm. Household perception on extreme climate conditions were a reflection of the quantitative climate data collected. Households were anxious (76%) that they will face negative impacts of climate change in future. Households were evenly distributed across the five vulnerability categories. Perceptions of communities to climate change should be considered by policy makers in advancing strategies to mitigate impacts of climate change. Vulnerability of farmers to climate change could be reduced by investing in early warning systems, providing farmers with information on climate change and farmers seeking alternative livelihood options rather than agriculture. Household specific interventions should be considered in mitigating climate change.

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“…The fuzzy objective function (1) described in [19][20][21][22][23][24] appeared to be one of the best ways for the fuzzification of data matrix. …”

Section: Fuzzy Principal Component Analysis-first Component (Fpca-1)mentioning

confidence: 99%

Rime samples characterization and comparison using classical and fuzzy principal components analysis

et al. 2008

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The main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied.

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Principal component analysis versus fuzzy principal component analysisA case study: the quality of danube water (1985–1996)

Cited by 159 publications

References 21 publications

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

Assessing rural farmers perceptions and vulnerability to climate change in uMzinyathi District of Kwazulu-Natal, South Africa

Rime samples characterization and comparison using classical and fuzzy principal components analysis

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