Are a special architecture of neural networks that

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Unformatted text preview: centroids t P1 . . . t Pk are stable 6: return set P : = { P1 , . . . , Pk }, of clusters. similarity in step three (cf. section 2.1). Furthermore the abort criterion of the loop in step five can be chosen differently e.g. by stopping after a fix number of iterations. Bi-Section-k-means One fast text clustering algorithm, which is also able to deal with the large size of the textual data is the Bi-Section-KMeans algorithm. In Steinbach et al. (2000) it was shown that Bi-Section-KMeans is a fast and high-quality clustering algorithm for text documents which is frequently outperforming standard KMeans as well as agglomerative clustering techniques. Bi-Section-KMeans is based on the KMeans algorithm. It repeatedly splits the largest cluster (using KMeans) until the desired number of clusters is obtained. Another way of choosing the next cluster to be split is picking the one with the largest variance. Steinbach et al. (2000) showed neither of these two has a significant advantage. are a special architecture of neural networks that cluster high-dimensional data vectors according to a similarity measure. The clusters are arranged in a low-dim...
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.

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