Another way to cluster the map units of the SOM is based on the assumption that there are always some neurons which lie between the clusters. These will get fewer hits from an input data set than neurons in the clusters, so the borders between clusters are indicated by low-hit areas [53]. There are however several defects with this method, for example the fact that because the SOM approaches the probability density function of the input data, in the ideal case there will be an equal number of hits from the training data in each unit of the map.

Having labeled samples naturally opens new possibilities. For example all units having a certain amount of hits from a certain class can be joined to form one cluster. However, since the SOM is based on unsupervised learning this topic will not be discussed here.

An interesting way to postprocess the SOM was proposed by Pedrycz *
et al* [33]. They used fuzzy sets and linguistic
variables to analyze interrelationships between different vector
components. Each component was expressed with fuzzy variables and
associated linguistic labels, e.g. "Large", "Medium" and "Small". A
linguistic description for a sample vector was achieved by aggregating
these labels from all components. Using the fuzzy variables the
membership value of each map unit for each description could be
calculated, and by selecting the descriptions with largest spread and
highest membership values, the map could be interpreted
linguistically.

Tue May 27 12:40:37 EET DST 1997