Both models have the DTD element ClusteringModel as the toplevel type and they share many other element types. A cluster model basically consists of a set of clusters. For each cluster a center vector can be given. In center-based models a cluster is defined by a vector of center coordinates. Some distance mesure is used to determine the nearest center, that is the nearest cluster for a given input record.
Some similarity measure is used to determine the best matching cluster for a given record.
The center vectors then only approximate the clusters. The model must contain information on the distance or similarity measure used for clustering. It may also contain information on overall data distribution, such as covariance matrix, or other statistics. The attribute modelClass specifies whether the clusters are defined by center-vectors or whether they are defined by the statistics. The latter is used by demographic clustering.