By Markus Franke
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Extra resources for An update algorithm for restricted random walk clusters
If in one of these cases the node created by this procedure has more than B children, it is split by choosing the pair of child nodes with the lowest pairwise similarity and using them as seeds for the newly created clusters. Clustering with Cluster Similarity Histograms Hammouda and Kamel [HK03] have proposed the similarity histogram-based overlapping clustering (SHC) algorithm that is claimed to guarantee a high degree of coherency for each cluster at all times. A cluster similarity histogram for a cluster is defined as having a number B of bins hi that correspond to intervals [sli , sui ) with sli and sui the lower and upper limit of bin hi .
Given the fact that in the extremal case, no data is ever discarded, it is quite a challenge to efficiently keep clusters derived from these data sets up to date. A general list of requirements for algorithms that cluster data arriving as streams has been given by Barbar´a [Bar02]. In general, when dealing with streams, it is assumed that the data that has arrived up to the current point in time is already included in the clustering. New data must be integrated with as little cost as possible. Therefore, the following criteria should be met by the algorithm: 42 CHAPTER 2 1.
GLF89] propose to use an incremental hill climbing learning algorithm in order to cope with the dynamics of the object set and to reduce the memory requirements. In this case, the landscape the hill climber must cross is the space of all concept hierarchies, the altitude or quality of a single solution being determined according to the fit between the data presented so far and the hierarchy. Contrarily to static hill climbing methods, the hill climber in the context of incremental conceptual clustering is confronted with a changing “landscape” in every step.