By Markus Franke
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Additional info for An update algorithm for restricted random walk clusters
The input order of the objects should not play a role for the results produced by the algorithm. 2 Handling Changing Similarities There is actually quite little literature on the subject of objects that can change their pairwise similarities. g. [CKT06]), and the data base literature provides some further examples for this kind of dynamic clustering [MK94, BS96, DG96, DFR+ 01]. 3, offer some distributed dynamic cluster algorithms, but their quality is in most cases limited in favor of an easy computation and low communication overhead.
Phase one builds the CF tree while scanning the data set. Zhang et al. claim that at any point in time during the CF tree construction, the tree represents a good clustering of the data processed so far. As a consequence, the algorithm is able, using only the first phase, to work on dynamically growing data sets. The static algorithm was evaluated against the CLARANS cluster algorithm [KR90] which it outperformed both with respect to the cluster quality and the run time. The dynamic version copes only with new objects and has an inferior quality compared to the static one which would necessitate a reclustering.
The result is a cluster hierarchy formed over the course of the algorithm’s execution. A cluster at a given level is characterized by a sphere around its centroid; the radius of the sphere is the maximum distance between the centroid and the cluster members. Obviously the spheres may overlap, even if the resulting clusters are disjunctive in terms of the objects they contain. Based on the GRACE algorithm, the authors have developed the GRIN algorithm. The idea is to use the GRACE algorithm to obtain an initial clustering for a randomly selected subset of constant size and to assign the remaining original objects as well as new objects to these clusters.
An update algorithm for restricted random walk clusters by Markus Franke