An update algorithm for restricted random walk clusters by Markus Franke

By Markus Franke

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Finally, if none of the above steps succeeds, the new object is put into the tentative outlier buffer. When a threshold on the number of objects in the tentative outlier buffer is reached, the object set has to be reclustered using GRACE as described above using the old leaf clusters and the contents of the tentative outlier buffer as input objects. The time complexity for the first, static phase is O(n2 ) for n objects and constant if the dendrogram is constructed using a fixed-size sample. The update phase has a complexity of O(n) if the dendrogram cannot grow infinitely and of O(n2 ) if it does.

4. Uniformity of distribution of the documents in the clusters. 5. Efficiency: The addition – and possibly removal – of objects should be efficient and practical 6. Optimality for retrieval: The resulting clustering should allow an efficient and effective retrieval procedure. The algorithm developed by Can et al. [CO87, CO89, CD90, Can93, CFSF95] was motivated by a typical information retrieval (IR) problem: Given m documents described by n terms, find groups of similar documents. The input data is given as a feature matrix Dm×n where the entry dij is either a binary variable that denotes whether document i is described by term j, or it contains the weight of term j in document i.

The approach is enhanced in [COP03] where not only the current data chunk is used for k-median clustering, but also the result from previous iterations of the algorithm. CHAPTER 2 43 Gupta and Grossman [GG04] present GenIc, another single-pass algorithm that is inspired by the principles of evolutionary algorithms (cf. 2) and only supports insertions. The population consists of the cluster centers ci . As each data chunk arrives, the fitness of the cluster centers included in the current generation is measured as their ability to attract a new object p in this chunk.

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