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Unsupervised learning methods

The main characteristics of the method, developed in our group for reaction classification arc 1) the representation of a reaction by physicochemical values calculated for the bonds being broken and made during the reaction, and 2 use of the unsupervised learning method of a self-organi2ing neural network for the perception of similarity of chemical reactions [3, 4],... [Pg.545]

Multiple linear regression is strictly a parametric supervised learning technique. A parametric technique is one which assumes that the variables conform to some distribution (often the Gaussian distribution) the properties of the distribution are assumed in the underlying statistical method. A non-parametric technique does not rely upon the assumption of any particular distribution. A supervised learning method is one which uses information about the dependent variable to derive the model. An unsupervised learning method does not. Thus cluster analysis, principal components analysis and factor analysis are all examples of unsupervised learning techniques. [Pg.719]

In Section 8.5 it was shown that supervised learning methods can be used to build effective classification models, using calibration data in which the classes are known. However, if one has a set of data for which no class information is available, there are several methods that can be used to explore the data for natural clustering of samples. These methods can also be called unsupervised learning methods. A few of these methods are discussed below. [Pg.307]

Unsupervised learning methods - cluster analysis - display methods - nonlinear mapping (NLM) - minimal spanning tree (MST) - principal components analysis (PCA) Finding structures/similarities (groups, classes) in the data... [Pg.7]

Kohonen self-organizing map An unsupervised learning method of clustering, based on the k-means algorithm, similar to the first stage of radial basis function networks. Self-organized maps are used for classification and clustering. [Pg.176]

The learning algorithm can be performed in supervised or unsupervised mode. In order to avoid bias, it is advisable to start data mining investigations of datasets with unsupervised learning methods. [Pg.217]

This study clearly illustrates the power of the unsupervised learning method comprised in a Kohonen network as... [Pg.139]

To introduce grouping of analytical data based on unsupervised learning methods, that is, projection methods and cluster analysis... [Pg.135]

If an assignment of classes to patterns is not evident, then unsupervised Learning methods are often helpful. Methods of finding clusters in a multidimensional pattern space are used to find natural classes in a data set. Such methods are not trivial and always contain heuristic and arbitrary elements. Subjective parameters are necessary to control the size, shape and number of clusters for a certain problem. different representations of the data often give different clusters. [Pg.92]

Principal component analysis (PCA), as described in Chapter 4, is an unsupervised learning method which aims to identify principal components, combinations of variables, which best characterize a data set. Best here means in terms of the information content of the components (variance) and that they are orthogonal to one another. Each principal component (PC) is a linear combination of the original variables as shown in eqn (4.1) and repeated below... [Pg.95]

The advantage of unsupervised learning methods is that any patterns that emerge from the data are dependent on the data employed. There is no intervention by the analyst, other than to choose the data in the first place, and there is no attempt by the algorithm employed to fit a pattern to the data, or seek a correlation, or produce a discriminating function (see Chapter 7). Any groupings of points which are seen on a non-linear map, a principal components plot, a dendrogram, or even a... [Pg.107]

The SIMCA method is based on the construction of principal components (PCs) which effectively fit a box (or hyper-box in N dimensions) around each class of samples in a data set. This is an interesting application of PCA, an unsupervised learning method, to different classes of data resulting in a supervised learning technique. The relationship between SIMCA and PLS, another supervized principal components method, can be seen clearly by reference to Section 7.3. [Pg.145]


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