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Abstract

Analytical data of primary oxidized manganese ores were processed by statistical methods. Six hundred and twenty-one samples were measured (Mn, Fe, Si, and P); thus 2,426 assay data were available. The statistical pointer numbers, the distribution of the elements and the results of the correlational analysis showed the heterogeneity of the ore samples where the measured elements correlated weakly. The samples were grouped by the 4 elements to decrease the heterogeneity and the concentration of elements, and these relationships in the groups were examined. Very few and weak relationships were proved in the groups by the results of the correlational and regressional analysis. It is possible that not the heterogeneity of the samples but one or more syngenetic or postgenetic processes caused the absence of relationships. The multivariate statistical processes (principal component analysis, discriminance analysis) allow the determination of the background factors, namely which are the effects that produced the ore. Consequently — with high probability — the ore was formed by two processes. The most likely are hydrothermal and microbial ones (on the basis of geochemical results), but supergene enrichment processes can also be taken into consideration. Both hydrothermal and microbial processes played a significant role in the majority of the samples (81%), which are the ferruginous manganese ores. In the smaller group of samples (19%) the hydrothermal process predominates but the microbial one is also influential, namely for the low iron-bearing manganese ores of excellent quality.

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Abstract

Miocene siltstone with variable sand content and bentonitic clay is the most abundant sediments encountered at the metro construction site at Rákóczi Square (Budapest). Core logs, drilling reports and records of laboratory analyses were studied to better understand the local geology and to prepare a database on engineering geologic properties of the materials. Using this database, geologic sections were prepared and geomathematical methods were used to obtain a better correlation of the strata in the area and a reconstruction of the geologic evolution of the area. The samples were divided into five groups based on physical properties. These five parameters allowed the use of multivariate statistical methods as cluster and discriminant analysis. As a result it was possible to identify several types of lithotypes, including two bentonitic clays with substantially different properties, one fat clay, one medium clay and one sandy, lean clay and siltstone group.

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? In addressing this research question, we used a mixed-methods approach to increase transferability. In “Methods” section, we introduce two studies: clustering analysis and focus group interview . The “Results” section reports on the usage types

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Fourteen isolates of tomato powdery mildew (Oidium neolycopersici) and one isolate of the following species: Podosphaera fusca (= Sphaerotheca fusca), Erysiphe orontii (cucumber powdery mildews), Erysiphe cicho­ra­cearum (lettuce powdery mildew) and Erysiphe aquilegiae var. ranunculi (Ranunculus lingua powdery mildew) were used for com­parative morpho­logical studies. Basic characteristics of the anamorphs, including outer conidial wall patterns, were compared using light and scanning electron microscopy (SEM). In main morphological features, O. neo­lyco­persici was strongly differentiated from E. cichoracearum, E. orontii and P. fusca. However, based on morphological features (e.g. germination type; appres­sorium shape; morphology of conidiophores) O. neolyco­per­sici was close to E. aquilegiae var. ranunculi (both belong to Oidium subgen. Pseudo­idium) and it probably could be placed to Erysiphe sect. Erysiphe (= Erysiphe s. str.)

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Breckenridge, J. N. (1989): Replicating cluster-analysis — methods, consistency, and validity. — Multivar. Behavioral. Res. 24 : 147–161. Breckenridge J. N

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. 1976. Correlation between single ecological variables and vegetation by means of cluster analysis. Not. Fitosoc , 12: 77–82. Feoli E. Correlation between single ecological variables

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142 388 396 L. Kaufman and P.J. Rousseeuw Finding groups in data; An introduction to cluster analysis, John Wiley & Sons, New York, 1990

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16 Artis, M. - Zhang, W. (2002): Membership of EMU: A Fuzzy Clustering Analysis of Alternative Criteria. Journal of Economic Integration, 17: 54

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Summary

Chemometrics involves strategies to analyse multivariate data using interdisciplinary approaches aiming to extract relevant information from complex data. Chemometric strategies comprise both the pre-processing of the data, where the choice of methodology is domain-specific, and analysis of the resulting data after preprocessing using multivariate methodology. Although use of multivariate data analysis for gel electrophoresis images has increased substantially in the last decade, its use is still much less frequent than use of univariate approaches. Considering the complexity of the electrophoresis gel images and the multivariate nature of the proteome, applying multivariate data analysis for gel electrophoresis images gives information which is otherwise lost. This paper is written as a review and guideline of chemometric strategies used for analysis of gel electrophoresis images. The multivariate data analyses described are, however, also relevant for other proteome data, for example mass spectrometry, and for functional genomics in general.

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shells preparations. The chemical fingerprints of Moringa seed shells from various regions were established and investigated by similarity analysis (SA), principal component analysis (PCA) and hierarchical clustering analysis (HCA). The combination of

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