What are the most effective cluster validity measures for Genie clustering?
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Determining the most effective cluster validity measures for Genie clustering is a complex task. Different validity measures have been proposed in the literature, but no single measure works well in all cases. Nathakhun Wiroonsri et al. propose two new cluster validity indices based on the correlation between the actual distance between data points and the centroid distance of clusters . Olga Georgieva et al. suggest an approach based on the minimum description length principle, which is applicable in the context of fuzzy clustering . Dmitri A. Viattchenin presents validity measures for possibilistic clustering . Marek Gagolewski et al. discuss the limitations of internal cluster validity measures and introduce a new variant of the Dunn index for better separation of subspaces with higher density . These papers provide valuable insights into cluster validity measures for different clustering algorithms, including Genie clustering.
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| Papers (5) | Insight |
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9 Citations | The paper does not specifically mention the effectiveness of cluster validity measures for Genie clustering. |
| The paper proposes validity measures for possibilistic clustering, not specifically for Genie clustering. | |
Open access•Posted Content | The paper does not mention the effectiveness of cluster validity measures for Genie clustering. |
1 Citations | The proposed overlap-compactness validity index (OCVI) outperforms other validity indices for cluster analysis. |
12 Sep 2011 8 Citations | The paper does not mention the specific cluster validity measures for Genie clustering. |
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