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Title (journal) Data Rec., Storage & Processing. — 2012. — Vol. 14, N 1.
Pages 25-34>
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Title (article) The Clustering Method Based on the Consequential Running of k-Means with Calculation of the Distances to the Active Centroids
Authors Tkachenko O.M., Bilichenko N.O., Griyo Tukalo O.F., Dzis O.V.
Kiev, Ukraine
Annotation A variant of the clustering problem solution based on k-means algorithm is considered. This algo-rithm is widely used in many fields of science and technology. The main drawbacks of k-means algorithm are the clustering results dependence on the choice of the initial configuration of centroids (initialization) and convergence to local minimum of the objective function. The proposed improved k-means provides а solution close to the global minimum distortion by the sequential k-means running for centroids. A significant speed-up of operation is achieved by calculating the distances only to the active centroids and reducing the number of candidate vectors for the initial choice of the new centroid location. The advantage of this approach is more appreciable when a larger data set with higher dimension is used. The proposed algorithm should be used in the speech data clustering problems when creating code books. Fig.: 4. Refs: 8 titles.
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