By Markus Franke

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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.

Finally, a CF tree is a height-balanced tree whose dimensions are guided by the following parameters: The branching factor B determines the maximum number of child entries in a non-leaf node, the parameter L the maximum number of entries in a leaf node and the threshold factor T is the upper limit for either the diameter or radius of the cluster composed of the elements in one entry of a leaf node. If X = {X1 , . . , XN } is the set of all objects to be clustered, an entry Xc ⊆ X of a leaf node thus has the form Xc = {Xc1 , .

It is thus not clear whether the incremental algorithm is used starting from the first object or whether another method is employed to initialize the clusters. The authors have tested the method on two small data sets containing web documents and have found the performance of the algorithm superior to HAC, single-pass and k-nearest neighbors. Incremental Hierarchical Clustering Ribert et al. [REL99], motivated by the high memory requirements for the clustering of large data sets with more than 10,000 objects, propose an incremental hierarchical clustering method both for dynamic data bases and for handling large data sets.

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