Classification unsupervised

In this classification numerical operations are performed that search for natural groupings of the spectral properties of pixels as examined in an image. The computer selects the mean class and covariance matrices to be used in classification. Once the data is classified, the classified data are assigned to some natural and spectral classes and the spectral classes are converted to information classes of interest. Some of the clusters are meaningless as they represent mixed classes of earth surface materials. The unsupervised classification attempts to cluster the Dn values of the scene into natural boundaries using numerical operations. 

Unsupervised classification operates on the color composite made from bands 2, 3, and 4 specifying just six clusters (Fig. 13). 

The light buff colors associate with the marine waters but are also found in the mountains where shadows are evident in the individual band and color composite images. Red occurs where there is some heavy vegetation. Dark olive is found almost exclusively in the ocean against the beach. The orange, green, and blue colors have less discrete associations. 

The image in Fig. 14 shows bands 2, 3, and 4, in which 15 clusters are set up a different color scheme is chosen (Fig. 14). 

In this image many individual areas represented by clusters do not appear to correlate well. Unfortunately, what is happening is a rather artificial subdivision of spectral responses from small segments of the surface. Another composite, bands 4,7, and 1, shows a new classification with the same problems as the first, although sediment variation in the ocean is better discriminated (Fig. 15). 

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Unsupervised classification or projection can also be used for assessing the relationships between samples. In several studies, it has been shown that cell-type-specific MS profiles can be used to merge samples based on their multivariate molecular information to identify novel patient subgroupings with different clinical behavior (Figure 3B) (49,50). 

In unsupervised classification, the grouping of the objects is not known beforehand and finding such groupings is the purpose of the analysis. Classical methods to perform unsupervised classification include variants of cluster analysis [Mardia et al. 1979],... 

In chemometrics we are very often dealing not with individual signals, but with sets of signals. Sets of signals are used for calibration purposes, to solve supervised and unsupervised classification problems, in mixture analysis etc. All these chemometrical techniques require uniform data presentation. The data must be organized in a matrix, i.e. for the different objects the same variables have to be considered or, in other words, each object is characterized by a signal (e.g. a spectrum). Only if all the objects are represented in the same parameter space, it is possible to apply chemometrics techniques to compare them. Consider for instance two samples of mineral water. If for one of them, the calcium and sulphate concentrations are measured, but for the second one, the pH values and the PAH s concentrations are available, there is no way of comparing these two samples. This comparison can only be done in the case, when for both samples the same sets of measurements are performed, e.g. for both samples, the pH values, and the calcium and sulphate concentrations are determined. Only in that case, each sample can be represented as a point in the three-dimensional parameter space and their mutual distances can be considered measures of similarity. 

As supervised methods are considered in this chapter (see [13] for applying ETD to unsupervised classification problems) it is assumed that there are time series available for calibration of the static classification method. For this data the class affiliation of each measurement is known and it is further supposed that no transitions occur during calibration measurements. 

Scale dendrograms can in principle be applied to both unsupervised and supervised classification, however in this chapter only examples from unsupervised classification are included. 

Unsupervised classification - Cluster analysis. Unsupervised classification or cluster analysis is a way to find natural patterns in a data set. There are no independent true answers that can guide the classification and we are therefore restricted to construct a set of criteria or general rules that can highlight the interesting patterns in a data set. A data set consists usually of a set of objects that each are characterised by a feature vector x. To find patterns it is important to establish to what degree vectors are similar to each other. Similarity is often defined using a distance metric between object i and j... 

In the previous section, the classification problem was considered to be essentially that of learning how to make decisions about assigning cases to known classes. There are, however, different forms of classification problem, which may be tackled by unsupervised learning, or clustering. Unsupervised classification is appropriate when the definitions of the classes, and perhaps even the number of classes, are not known in advance, e.g., market segmentation of customers into similar groups who can then be targeted separately. 

The fuzzy C-mean (FCM) approach (Udupa and Samarasekera 1996 Bezdek 1948) is able to make unsupervised classification of data in a number of clusters by identifying different tissues in an image without the use of an explicit threshold. The FCM algorithm performs a classification of image data by computing a measure of membership, called fuzzy membership, at each pixel for a specified number of classes. The fuzzy membership function, con-... 

The unsupervised classification is too much of a generalization and the clusters only roughly match some of the actual classes. Its value is mainly as a guide to the spectral content of a scene to aid in making a preliminary interpretation prior to conducting the much more powerful supervised classification procedures. There are several types of unsupervised classification which are explained briefly below ... 

In terms of chemometrics, the concepts presented here fall into the category of supervised classification methods, because it must be known beforehand which group each specimen falls into. Unsupervised training methods also exist, in which samples are grouped based solely on the characteristics of the data. In order to use the methods presented here, the additional information, in other words, which samples belong to which group, must be known beforehand. It is conceivable, however, that that information could be derived from an unsupervised classification scheme and then applied to the supervised scheme. 

One should test for the internal data structure on the basis of unsupervised classification techniques (such as data clustering). This might be useful to determine whether the data principally allow the desired class assignments to be accomplished. 

Supervised and unsupervised classification for example PCA, K-means and fuzzy clustering, linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), fisher discriminant analysis (FDA), artificial neural networks (ANN). 

FIGURE 8 Unsupervised classification of almonds by using PCA. The scores surfaces and the corresptmding loadings are depicted in the left-most part whereas the right-most part of the figure denotes the PCI vs. PC2 plot of the mean spectrum for each almond. The colours of the score images are individually scaled in such a way that blue denotes intensity 0 whereas red denotes the maximum intensity 1. 

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