Clustering structure of the dataset is measured by the divisive coefficient. For each learning example ti, d(i) is defined as the diameter of the last cluster to which it belongs (before being split off as a single observation), divided by the diameter of the whole dataset. The divisive co...
Hierarchical clustering methods, which can be mainly categorized into agglomerative (bottom-up) and divisive (top-down) procedures, are well known [1–20]. In ag- glomerative procedures, each sample is initially assumed to be a cluster. The two nearest clusters (based on a distance measure ...
We present DIVCLUS-T on a small numerical and a small categorical example. DIVCLUS-T is then compared with two polythetic clustering methods: the Ward ascendant hierarchical clustering method and the k-means partitional method. The three algoritms are applied and compared on six databases of ...
Euclidean distance is one example of a dissimilarity measure. Contrast to similarity. Also see proximity and Euclidean distance. divisive hierarchical clustering methods. Divisive hierarchical clustering methods are top-down meth- ods for hierarchical clustering. All the data begin as a part of one ...