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Data unsupervised

The Kohonen network adapts its values only with respect to the input values and thus reflects the input data. This approach is unsupervised learning as the adaptation is done with respect merely to the data describing the individual objects. [Pg.458]

In unsupervised learning, the outcome is usually a hypothesis to then be tested, often usiag classification or prediction methods. If the unsupervised learning process suggests the presence of distinct clusters, the hypothesis can be tested by applyiag a classification method to the data. A low number of misclassified samples would tend to reinforce the hypothesis. [Pg.424]

Shen Q, Ren H, Fisher M, Bouley J, Duong TQ. Dynamic tracking of acute ischemic tissue fates using improved unsupervised isodata analysis of high-resolution quantitative perfusion and diffusion data. J Cereb Blood Flow Metab. 2004 24 887-897. [Pg.55]

Analytical results are often represented in a data table, e.g., a table of the fatty acid compositions of a set of olive oils. Such a table is called a two-way multivariate data table. Because some olive oils may originate from the same region and others from a different one, the complete table has to be studied as a whole instead as a collection of individual samples, i.e., the results of each sample are interpreted in the context of the results obtained for the other samples. For example, one may ask for natural groupings of the samples in clusters with a common property, namely a similar fatty acid composition. This is the objective of cluster analysis (Chapter 30), which is one of the techniques of unsupervised pattern recognition. The results of the clustering do not depend on the way the results have been arranged in the table, i.e., the order of the objects (rows) or the order of the fatty acids (columns). In fact, the order of the variables or objects has no particular meaning. [Pg.1]

In the following sections we propose typical methods of unsupervised learning and pattern recognition, the aim of which is to detect patterns in chemical, physicochemical and biological data, rather than to make predictions of biological activity. These inductive methods are useful in generating hypotheses and models which are to be verified (or falsified) by statistical inference. Cluster analysis has... [Pg.397]

Two examples of unsupervised classical pattern recognition methods are hierarchical cluster analysis (HCA) and principal components analysis (PCA). Unsupervised methods attempt to discover natural clusters within data sets. Both HCA and PCA cluster data. [Pg.112]

Inhomogeneities in data can be studied by cluster analysis. By means of cluster analysis both structures of objects and variables can be found without any pre-information on type and number of groupings (unsupervised learning, unsupervised pattern recognition). [Pg.256]

The data are dominated by a few low molecular weight components. Figure 15.11 presents an image that has been amplified by a factor of 30 many more components are visible. Figure 15.12 presents the same data amplified by another factor of 30 for a total amplification of 1000, and a sea of peaks is visible. An unsupervised routine was used to isolate all local maxima in the data 190 components were resolved with amplitude greater than 10 times the standard deviation of the background signal. [Pg.360]

Principal component analysis (PCA) can be considered as the mother of all methods in multivariate data analysis. The aim of PCA is dimension reduction and PCA is the most frequently applied method for computing linear latent variables (components). PCA can be seen as a method to compute a new coordinate system formed by the latent variables, which is orthogonal, and where only the most informative dimensions are used. Latent variables from PCA optimally represent the distances between the objects in the high-dimensional variable space—remember, the distance of objects is considered as an inverse similarity of the objects. PCA considers all variables and accommodates the total data structure it is a method for exploratory data analysis (unsupervised learning) and can be applied to practical any A-matrix no y-data (properties) are considered and therefore not necessary. [Pg.73]

Two generally different scenarios can be found for applications of machine learning technology so-called supervised and unsupervised learning. The difference is the presence or absence of observation of the desired output on a training data set. [Pg.74]

Methods for unsupervised learning invariably aim at compression or the extraction of information present in the data. Most prominent in this field are clustering methods [140], self-organizing networks [141], any type of dimension reduction (e.g., principal component analysis [142]), or the task of data compression itself. All of the above may be useful to interpret and potentially to visualize the data. [Pg.75]

A self-organizing Kohonen map of the total database of cleaved retrosynthetic fragments generated as the result of an unsupervised learning procedure (data not shown)indicates that the cleaved fragments occupy a wide area on the map, characterized... [Pg.298]

Method validation Basic method validation (short-term use, fit for purpose, little robustness), data will see expert eye prior to release Extended method validation, robustness and ruggedness tests important for unsupervised operation... [Pg.21]

The two pattern recognition techniques used In this work are among those usually used for unsupervised learning. The results will be examined for the clusters which arise from the analysis of the data. On the other hand, the number of classes and a rule for assigning compounds to each had already been determined by the requirements of the mixture analysis problem. One might suppose that a supervised approach would be more suitable. In our case, this Is not so because our aim Is not to develop a classifier. Instead, we wish to examine the data base of FTIR spectra and the metric to see If they are adequate to help solve a more difficult problem, that of analyzing complex mixtures by class. [Pg.161]


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Unsupervised

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