Pattern Recognition, 4th Edition

16.6. Clustering Tendency

As discussed in the introduction of the chapter, almost all the clustering algorithms introduced in the previous sections share an annoying feature. That is, they impose a clustering structure on a data set X even though the vectors of X do not exhibit such a structure. Thus, in order to prevent a misleading interpretation of the structure of the data set X, it would be more sensible to check first whether X possesses a clustering structure. If this is the case, then one may proceed by applying a clustering algorithm to X. Otherwise, cluster analysis is likely to lead to misleading results. The problem of determining the presence or the absence of a clustering structure in X is called clustering tendency. Usually, ...