Unsupervised techniques

Hierarchical cluster analysis (HCA) is an unsupervised technique that examines the inteipoint distances between all of the samples and represents that information in the form of a twcKlimensional plot called a dendrogram. These dendrograms present the data from high-dimensional row spaces in a form that facilitates the use of human pattern-recognition abilities. 

The goal of unsupervised techniques is to identify and display natural groupings in the data without imposing any prior class membership. Even when the ultimate goal of the project is to develop a supervised pattern recognition model, we recommend the use of unsupervised techniques to provide an initial view of the data. 

While PCA is an unsupervised technique, the classification methods are supervised techniques. In these techniques the category of each of the objects on which the model is built must be specified in advance. 

Metabolomic analysis can lead to a large amount of data and there are numerous statistical approaches that can be used to interrogate the dataset. Approaches can be broadly divided into supervised and unsupervised techniques. Supervised techniques use elassiheation information about the data whereas unsupervised teehniques use no prior information. 

PCA is an unsupervised technique whose main idea is to explore data analysis and to reduce dimensionality with a minimum loss of information. This is achieved by projecting the data onto fewer dimensions that are chosen to exploit the relationships between the variables. It also measures qualitative associations between variables. To analyze the results, a line of best fit through a system of points in space is obtained. This technique allows the similarities and differences between objects and samples to be better assessed [30]. 

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