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Pattern and cluster analysis

The purpose of most pattern detection methods is to represent the variation in a data set into a more manageable form by recognizing classes or groups. The data typically consist of a set of objects described by a number of characters. In microarray data, for example, an object is a gene, while a character is the level of expression of that gene under a particular condition.

If the objects were always described by only two or three characters, there would not be much need for pattern detection methods. Just plotting the data in two or three dimensions, respectively, would be sufficient to distinguish groups. However, typically, objects are characterised by more than three characters, so simply plotting the data is not possible. Other ways need to be found to represent the data. There are basically two approaches that can be taken:

The book chapter Theoretical Aspects of Pattern Analysis gives a simple introduction to both principal component and cluster analysis. Applications of both types of analyses can be found in the papers listed below.



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