A novel cluster validity index based on augmented non-shared nearest neighbors

Duan, Xinjie; Ma, Yan; Zhou, Yuqing; Huang, Hui; Wang, Bin

doi:10.1016/j.eswa.2023.119784

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…Most CVIs indicate that a good partition produces a small compactness value and a high separability value. However, the existing CVIs are vulnerable to validating cluster results when the shapes of the clusters are not spherical clusters [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…There have been few studies on uncertain data. Moreover, relatively new CVIs are also being designed to incorporate mathematical theories into pre-existing CVIs, such as the K-nearest neighbor algorithm, which is used to compute compactness and separation by taking into account shared/non-shared data pairs [ 10 ], and principal component analysis, which is used to capture the geometry of the clusters [ 16 ]; or to develop clustering algorithms to cluster more well-separated clusters [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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Cluster Validity Index for Uncertain Data Based on a Probabilistic Distance Measure in Feature Space

Baek

Tavakkol

et al. 2023

Sensors

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Cluster validity indices (CVIs) for evaluating the result of the optimal number of clusters are critical measures in clustering problems. Most CVIs are designed for typical data-type objects called certain data objects. Certain data objects only have a singular value and include no uncertainty, so they are assumed to be information-abundant in the real world. In this study, new CVIs for uncertain data, based on kernel probabilistic distance measures to calculate the distance between two distributions in feature space, are proposed for uncertain clusters with arbitrary shapes, sub-clusters, and noise in objects. By transforming original uncertain data into kernel spaces, the proposed CVI accurately measures the compactness and separability of a cluster for arbitrary cluster shapes and is robust to noise and outliers in a cluster. The proposed CVI was evaluated for diverse types of simulated and real-life uncertain objects, confirming that the proposed validity indexes in feature space outperform the pre-existing ones in the original space.

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Section: Introductionmentioning

confidence: 99%