Şenol Ali
Department of Computer Engineering, Faculty of Engineering, Tarsus University, Mersin, Turkey.
Comput Intell Neurosci. 2022 Jun 8;2022:4059302. doi: 10.1155/2022/4059302. eCollection 2022.
The cluster evaluation process is of great importance in areas of machine learning and data mining. Evaluating the clustering quality of clusters shows how much any proposed approach or algorithm is competent. Nevertheless, evaluating the quality of any cluster is still an issue. Although many cluster validity indices have been proposed, there is a need for new approaches that can measure the clustering quality more accurately because most of the existing approaches measure the cluster quality correctly when the shape of the cluster is spherical. However, very few clusters in the real world are spherical. Therefore, a new Validity Index for Arbitrary-Shaped Clusters based on the kernel density estimation (the VIASCKDE Index) to overcome the mentioned issue was proposed in the study. In the VIASCKDE Index, we used separation and compactness of each data to support arbitrary-shaped clusters and utilized the kernel density estimation (KDE) to give more weight to the denser areas in the clusters to support cluster compactness. To evaluate the performance of our approach, we compared it to the state-of-the-art cluster validity indices. Experimental results have demonstrated that the VIASCKDE Index outperforms the compared indices.
聚类评估过程在机器学习和数据挖掘领域非常重要。评估聚类的质量可以显示任何提出的方法或算法的胜任程度。然而，评估任何聚类的质量仍然是一个问题。尽管已经提出了许多聚类有效性指标，但仍需要新的方法来更准确地衡量聚类质量，因为大多数现有方法在聚类形状为球形时才能正确测量聚类质量。然而，现实世界中很少有聚类是球形的。因此，该研究提出了一种基于核密度估计的任意形状聚类新有效性指标（VIASCKDE指标）来克服上述问题。在VIASCKDE指标中，我们使用每个数据的分离度和紧致度来支持任意形状的聚类，并利用核密度估计（KDE）对聚类中密度较高的区域给予更大权重以支持聚类紧致度。为了评估我们方法的性能，我们将其与最先进的聚类有效性指标进行了比较。实验结果表明，VIASCKDE指标优于所比较的指标。
