A New Cluster Validity Index Based on the Adjustment of Within-Cluster Distance

Li, Qi; Yue, Shihong; Wang, Yaru; Ding, Mingliang; Li, Jia

doi:10.1109/access.2020.3036074

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Such as DBCV [18], Xie-Beni (XB) [27], CDbw [10], S Dbw [9], and RMSSTD [8]. Besides, new cluster validity indices keep emerging, such as the CVNN [16], CVDD [11], DSI [6], SCV [28], AWCD [14] and VIASCKDE [23].…”

Section: Related Work 21 Internal Clustering Evaluation Indexmentioning

confidence: 99%

Clustering with Neural Network and Index

Liu¹

2022

Preprint

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A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural Network to cluster data points. Training of the Neural Network mimics supervised learning, with an internal clustering evaluation index acting as the loss function. An experiment is conducted to test the feasibility of the new model, and compared with results of other clustering models like K-means and Gaussian Mixture Model (GMM).

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Section: Related Work 21 Internal Clustering Evaluation Indexmentioning

confidence: 99%

Clustering with Neural Network and Index

Liu¹

2022

Preprint

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“…The Silhouette Index (SI) [ 30 ], Dunn Index [ 31 ], Davies–Bouldin (DB) [ 32 ], Calinski-Harabasz (CH) [ 33 ], Xie-Beni (XB) [ 34 ], S_Dbw [ 35 ], and RMSSTD [ 36 ] can be mentioned as primary cluster validity indices. Besides, there are many new cluster validity indices such as the CVNN [ 37 ], CVDD [ 38 ], DSI [ 39 ], SCV [ 40 ], and AWCD [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation

Şenol

2022

Computational Intelligence and Neuroscience

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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.

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