File:Clusters generated with different density-based algorithms.png

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English: The clusterization of Mitosis and M. Liu et al. algorithms

recognize exactly the amount of genuine clusters, which are 9, so that all noise in the image is included within these genuine clusters. This can be bad in some occasions such as when noise is expected to be removed or when it should not be aggregated in a genuine cluster. On the other hand, the clusterization generated by Chameleon recognizes 9 genuine clusters as well as 3 other clusters that contain outliers. Chameleon and k-MS are the only algorithms capable of segregating outliers in different clusters. k-MS goes even further, it indicates whether the clusters can be formed given a predefined k amount of

clusters.
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Author Flamero22

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