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.