t pk of clusters p1 pk 5 until cluster centroids

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Unformatted text preview: f the k centroids (also called cluster prototype). Step four calculates a new centroids on the basis of the new allocations. We repeat the two steps in a loop (step five) until the cluster centroids do not change any more. The algorithm 5.1 corresponds to a simple hill climbing procedure which typically gets stuck in a local optimum (the finding of the global optimum is a NP complete problem). Apart from a suitable method to determine the starting solution (step one), we require a measure for calculating the distance or Band 20 – 2005 41 Hotho, Nürnberger, and Paaß Algorithm 1 The KMeans algorithm Input: set D, distance measure dist, number k of cluster Output: A partitioning P of the set D of documents (i. e., a set P of k disjoint subsets of D with P∈P P = D). 1: Choose randomly k data points from D as starting centroids t P1 . . .t Pk . 2: repeat 3: Assign each point of P to the closest centroid with respect to dist. 4: (Re-)calculate the cluster centroids t P1 . . . t Pk of clusters P1 . . . Pk . 5: until cluster...
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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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