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A new improved cluster validity indexing technique: harnessed from Goodman-Kruskal validity index

机译:一种新的改进的聚类有效性索引技术:利用Goodman-Kruskal有效性索引

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摘要

The true potential of clustering techniques is not harnessed optimally because of several reasons. Clustering is implemented either on the pre-classified datasets or if implemented on unclassified datasets, it remains unacceptable because its validity cannot be established. Cluster validity techniques come to rescue in the latter cases. Several internal and external cluster validity indices are studied and used to validate the clustering techniques. Moreover, the validity of the indexing techniques is needed to be established first. The current work suggests an improvement over the Goodman-Kruskal indexing technique and establishes its validity by applying it on several benchmark datasets. Hence, it suggests a new cluster validity indexing technique.
机译:由于多种原因,未能充分利用群集技术的真正潜力。聚类是在预分类的数据集上实现的,或者在未分类的数据集上实现的,由于无法确定其有效性,因此仍然不能接受。在后一种情况下,可以使用聚类有效性技术。研究了几个内部和外部聚类有效性指标,并将其用于验证聚类技术。而且,首先需要确定索引技术的有效性。当前的工作提出了对Goodman-Kruskal索引技术的改进,并通过将其应用于几个基准数据集来确定其有效性。因此,它提出了一种新的聚类有效性索引技术。

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