Clustering and Detection of Similarities

In clustering, data vectors are grouped together into clusters on the basis of intrinsic similarities between these vectors. In contrast to classification, no classes are defined beforehand. A commonly used method is the application of Kohonen networks (cf. Section 9.5.3). 

One application of clustering could, for example, be the comparison of compound libraries A training set is chosen which contains members of both libraries. After the structures are coded (cf. Chapter 8), a Kohonen network (cf. Section 9.5.3) is trained and arranges the structures within the Kohonen map in relation to their structural similarity. Thus, the overlap between the two different libraries of compounds can be determined. 

---