The Clustering Method Based on the Consequential Running of k-Means with Calculation of the Distances to the Active Centroids
A variant of the clustering problem solution based on k-means algorithm is considered. This algorithm 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 (initia...
Збережено в:
| Дата: | 2012 |
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| Автори: | , , , |
| Формат: | Стаття |
| Мова: | Ukrainian |
| Опубліковано: |
Інститут проблем реєстрації інформації НАН України
2012
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| Теми: | |
| Онлайн доступ: | http://drsp.ipri.kiev.ua/article/view/311801 |
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| Назва журналу: | Data Recording, Storage & Processing |
Репозитарії
Data Recording, Storage & Processing| Резюме: | A variant of the clustering problem solution based on k-means algorithm is considered. This algorithm 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 1, 2,..., k 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 cen- troid 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. |
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