Two cluster validity indices for the LAMDA clustering method

Valderrama, Javier Fernando Botía; Valderrama, Diego José Luis Botía

doi:10.1016/j.asoc.2020.106102

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…Evaluating the quality of clustering methods is as important as the clustering algorithms themselves. Here are some clustering validity indices (CVI) [1,2,3,4,5] that evaluate the quality of the solution, and these CVIs have commonly been used in many fields [6,7,3]. Literature [8,9] proposed validity index based on density, which was computed by using Euclidean Distance.…”

Section: Introductionmentioning

confidence: 99%

A New Cluster Validity Index for Fuzzy Clustering Using Separation and Compactness

Chen¹,

Zhong²,

Chen

et al. 2023

Preprint

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Aiming at weak point of classical validity index based on Euclidean distance, we proposed a new validity index which based on Non-Euclidean Distance. First of all, we constructed compact index by using improved membership degree and then made use of anti-close degree to constructed separation index. Finally, a new validity index based on these was proposed. This method overcame the drawbacks of classical validity index based on Euclidean distance, which did not correctly reflect the relation of intra-class and inter-class. Experiments on different datasets show that new method has good performance in noise data environment. The capacity of new method is superior to others with regard to cope with overlap dataset. New method has more obvious superiority especially on high dimensional dataset.

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