Clustering datasets with missing values using modified K-medoids algorithm
A modification was done to the Euclidean distance to compute distance for incomplete data points, at the same time flagging them so that the algorithm will avoid choosing them as cluster medoids. This resulted to the Modified K-medoids clustering algorithm applied with pre-processing methods, namely...
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| Natura: | Tesi |
| Lingua: | English |
| Pubblicazione: |
2008
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