A modified k-means clustering algorithm with Mahalanobis distance for clustering incomplete data sets
Cluster analysis is an art of finding grounds in data in such a way that objects in the same group are similar to each other, whereas objects in different groups are as dissimilar as possible. The most commonly used clustering algorithm is the K-means with Euclidean distance. However, such distance...
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| Format: | Thèse |
| Langue: | anglais |
| Publié: |
2009
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