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All elements that belong to the subtree are attached as direct children of the leaf cluster. Optionally, outliers can be removed and the dendrogram can be rebuilt in their absence. This step is especially recommended if the outliers have a strong influence on the clustering. With the dendrogram completed, the remaining data points as well as the ones arriving at a later point of time can be integrated. If a new datum falls into the sphere of a cluster, it is integrated. If it falls into the spheres of several clusters, the cluster exerting the highest gravitational force, determined from the cluster’s weight and distance, is selected to receive the object.

Furthermore, the list of cluster representations should optimally have a constant size; even a linear growth of the clusters’ representation list is considered intolerable. 2. Fast incremental processing of new data points: In most cases, comparing a new point to all points in each cluster is not feasible. Thus the function should use the compact representation of the clusters. Furthermore, it should display a “good performance” in deciding about the membership of the new objects in the respective clusters.

This increases the probability of the algorithm sticking to local optima. Roure and Talavera [RT98] have therefore suggested the “not-yet” strategy for CHAPTER 2 31 various clustering algorithms, where each insertion is evaluated in terms of the algorithm’s cluster quality criterion. e. the confidence of adding the new object, is above a given threshold, it is inserted, else it is stored in a buffer to be inserted later on. The idea has a certain similarity to simulated annealing (cf. 3) and has been shown by the authors to considerably reduce the influence of a bad instance ordering.

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