Cluster analysis

Bratchell, N.

doi:10.1016/0169-7439(87)80054-0

Cited by 122 publications

(55 citation statements)

References 16 publications

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“…Principal component (p.c.) analysis (Malinowski and Howery, 1980) and cluster analysis (Bratchell, 1989) of mass spectrometric (=internal) and elemental, wet-chemical, biochemical, microbiological (=external) data were carried out by subroutines of the A R T H U R package. Cluster analysis was performed with calculated p.c.…”

Section: Chemometrical Evaluation Of the Data Setsmentioning

confidence: 99%

“…When using cluster analysis, the use of a group of highly correlated variables in the original data which provide the same information on some aspects of the data may give greater weight to that aspect than to other equally important aspects (Bratchell, 1989). In order to reduce problems arising from highly correlated variables, cluster analysis is applied to a subset of the largest principal components (Eigenvalues >1) Z of internal and external data.…”

Section: Integrated Approach: Comparison With Elemental Wet-chemicalmentioning

confidence: 99%

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Influence of agricultural soil management on humus composition and dynamics: Classical and modern analytical techniques

Schulten

Hempfling

1992

Plant Soil

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In-source pyrolysis-field ionization mass spectrometry (Py-FIMS), in combination with complementary elemental, wet-chemical, biochemical, and microbiological data, has been used to characterize humus composition and dynamics in soil samples from several field plots that have been cultivated in long-term experiments under different management conditions. Thermograms and Py-FI mass spectra of whole-soil samples from field plots that under very different management show significant differences in humus composition, which may be due to varying stages of decomposition of plant residues and humus genesis. The intensity of soil management significantly affects high-molecular-weight subunits such as dimeric lignin-, arylalkyl-, and aliphatic constituents, even though humus quantity is similar for plots under more practically oriented management, such as crop rotation. The differences in molecular humus subunits of soil samples from different plots, in combination with complementary data, demonstrated that less parent (i.e. primary) material is incorporated in the humus matrix under intense soil management conditions. Samples from different field plots can thus be objectively differentiated on the basis of humus properties using multivariate statistical techniques such as principal component and cluster analyses. This statistical discrimination, using Py-FI mass spectra of the samples, corresponds well with microbial biomasses but is somewhat inconsistent with elemental data and results of chemical degradation procedures. The microflora populations in soils under intense management are limited by low availability and/or quality of carbon substrates. The resulting restricted internal nitrogen cycle causes those soils to have a reduced capacity to immobilize N, leading to relative enrichment of heterocyclic nitrogen compounds that are resistant to mineralization.

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Section: Chemometrical Evaluation Of the Data Setsmentioning

confidence: 99%

Section: Integrated Approach: Comparison With Elemental Wet-chemicalmentioning

confidence: 99%

Influence of agricultural soil management on humus composition and dynamics: Classical and modern analytical techniques

Schulten

Hempfling

1992

Plant Soil

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“…As previously mentioned, cluster analysis can be a difficult and time-consuming step [29][30][31], and another strategy for making the selection is therefore a combination of methods 3⋅4 and 3⋅1 in order to overcome problems with the cluster analysis. First the 'natural cluster' from the patent series is used to create classes.…”

Section: Patent Cluster Selectionmentioning

confidence: 99%

“…Since the aim is to select a subset for screening of a library, it is not desirable to divide the data into too many clusters, because this might increase the final number of compounds required in the testing. No reliable method for defining and identifying the number of clusters in a data set exists [31]; however, a rough guideline is to identify natural classes in an initial screening. The final choice must be subjectively judged by a visual inspection in the structural space.…”

Section: Cluster-based Selectionmentioning

confidence: 99%

Strategies for subset selection of parts of an in‐house chemical library

Andersson

Sjöstróm

Wold

et al. 2001

Journal of Chemometrics

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SUMMARYWhen a company decides to perform biological testing of their 'in-house' library, i.e. compounds which have been synthesized or purchased over the years, it is usually not feasible or desirable to test all of them using e.g. high-throughput screening (HTS). The limitation is the usually high number of compounds to test (10 4 -10 6 ) leading to practical limitations and high costs in terms of both material costs and disposal considerations. Therefore it is often desirable to make a selection of which compounds to include in the biological testing. A challenge is how to make this selection in order to cover the structural space of the in-house library as well as possible. Here we present and discuss different selection strategies based mainly on statistical molecular design (SMD). These methods require different prior information about the compounds under investigation, e.g. characterization of the chemical structure, affinity/biological activity data or neither of these. Which method to be used is largely problem-dependent, i.e. the composition and origin of the library, and hence the structural space, are of great importance. Chemical and biological knowledge about the system under investigation should as far as possible be considered when making the final decision on which method to apply.

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“…Outro método que se apresenta útil na classificação de diferentes amostras é a análise hierárquica por agrupamentos [1]. Tal como o nome indica, a análise de clusters (agrupamentos) tem como objetivo agrupar os dados de forma a permitir identificar semelhanças entre os objetos.…”

Section: -Análise Hierárquica Por Agrupamentos (Hca)unclassified