Pattern recognition, unsupervised

A more formal method of treating samples is unsupervised pattern recognition, mainly consisting of cluster analysis. Many methods have their origins in numerical taxonomy. 

These principles can be directly applied to chemistry. It is possible to determine similarities in amino acid sequences in myoglobin in a variety of species. The more similar the species, die closer is die relationship chemical similarity mirrors biological similarity. Sometimes die amount of information is so huge, for example in large genomic or crystallographic databases, diat cluster analysis is die only practicable way of searching for similarities. 

Unsupervised pattern recognition differs from exploratory data analysis in diat the aim of the methods is to detect similarities, whereas using EDA diere is no particular prejudice as to whether or how many groups will be found. Cluster analysis is described in more detail in Section 4.4. 

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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. 

D. Coomans and D.L. Massart, Potential methods in pattern recognition. Part 2. CLUPOT an unsupervised pattern recognition technique. Anal. Chim. Acta., 133 (1981) 225-239. 

Mathematically, this means that one needs to assign portions of an 8-dimensionaI space to the three classes. A new sample is then assigned to the class which occupies the portion of space in which the sample is located. Supervised pattern recognition is distinct from unsupervised pattern recognition. In the latter one applies essentially clustering methods (Chapter 30) to classify objects into classes that are not known beforehand. In supervised pattern recognition, one knows the classes and has to decide in which of those an object should be classified. 

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). 

Pattern Recognition A process of examining the relationships between samples and/or variables in a data set. Unsupervised pattern-recognition tools are used to determine if there are groupings of similar samples in a data set. Supervised pattern-recognition tools are used to classify unknown samples as more likely of type A or type B. 

Supervised versus Unsupervised Pattern Recognition In some situations the class membership of the samples is unknown. For example, an analyst may simply want to examine a data set to see what can be learned. Are there any groupings of samples Are there any outliers (i.e., a small number of samples that are not grouped with the majority) Even if class information is known, the analyst may want to identify and display natural groupings in the data without imposing class membership on the samples. For example, assume a series of spectra have been collected and the goal is to... 

The answers to these questions will usually be given by so-called unsupervised learning or unsupervised pattern recognition methods. These methods may also be called grouping methods or automatic classification methods because they search for classes of similar objects (see cluster analysis) or classes of similar features (see correlation analysis, principal components analysis, factor analysis). 

Table 12 Distance Metrics Used in Unsupervised Pattern Recognition (i.e., Clustering)...

The chemist also wishes to relate samples in a similar manner. Can protein sequences from different animals be related and does this tell us about the molecular basis of evolution Can the chemical fingerprint of wines be related and does this tell us about the origins and taste of a particular wine Unsupervised pattern recognition employs a number of methods, primarily cluster analysis, to group different samples (or objects) using chemical measurements. 

Walley W. J. and O Connor M. A. (2000) Unsupervised pattern recognition for the interpretation of ecological data. 2nd Int. Conf. on Applications of Machine Learning to Ecological Modelling, Adelaide, November 2000. J. Ecol. Model., 146(1-3), 219-230. 

Boutros, P.C. and Okey, A.B. (2005) Unsupervised pattern recognition an introduction to the whys and wherefores of clustering microarray data. Brief. Bioinform. 6, 331-343. 

In the narrow sense, cluster analysis should not be confused with classification methods, where unknown objects are assigned to existing classes. Cluster analyses belong to the methods of unsupervised learning or unsupervised pattern recognition. 

One possibility to speedup the search is preliminary sorting of the data sets. Here, the methods of unsupervised pattern recognition are used, for example, principal component and factor analysis, cluster analysis, or neural networks (cf. Sections 5.2 and 8.2). The unknown spectrum is then compared with every class separately. 

Micheh-Tzanakou, E. (2000). Supervised and Unsupervised Pattern Recognition Feature Extraction and Computational Intelligence, CRC Press, Boca Raton, FL. 

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