A survey of fuzzy clustering validity evaluation methods

Wang, Hongyu; Wang, Jie-Sheng; Wang, Guan

doi:10.1016/j.ins.2022.11.010

Cited by 30 publications

(5 citation statements)

References 107 publications

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“…According to the results of the comparative analysis, the choice was made in favor of the fuzzy-set approach [10][11]. It should be noted that this approach is actively used today to model complex situations in various industry specifics [12][13][14][15].…”

Section: Methodsmentioning

confidence: 99%

Creation of a methodology for assessing the impact of the intellectual capital management mechanism on key indicators of sustainable development of an enterprise

Akatov,

Mingaleva,

Andronov

et al. 2023

E3S Web Conf.

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Annotation. The sustainable development of an enterprise is increasingly dependent on the creation of effective intellectual capital management systems, on the applied intellectual capital management mechanism. In turn, this implies the creation of a reliable mathematical tool for monitoring and evaluating this efficiency, as well as assessing the impact of the mechanism for managing the intellectual capital of an enterprise on key indicators of its sustainable development. The article presents a description of the creation of a methodology and a model for such an assessment. The fuzzy-multiple approach is used as the main one. The article presents a cognitive model of the relationship between assessment indicators, a procedure for assessing the relationship, models for assessing the influence of factors, the procedure for identifying and evaluating key results, identifying critical factors, and calculating the final complex indicator.

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Section: Methodsmentioning

confidence: 99%

Creation of a methodology for assessing the impact of the intellectual capital management mechanism on key indicators of sustainable development of an enterprise

Akatov,

Mingaleva,

Andronov

et al. 2023

E3S Web Conf.

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“…C F and inter-cluster separation S F are evaluation indexes of clustering algorithms, which are commonly used to judge the quality of clustering effect and evaluate the advantages and disadvantages of clustering algorithms after clustering is completed [33]. A good clustering algorithm can produce high-quality clusters with high similarity within clusters and low similarity between clusters.…”

Section: Intra-cluster Compactnessmentioning

confidence: 99%

Energy efficient clustering routing algorithm based on improved FCM

Sun,

Meng,

Zhao

et al. 2024

Preprint

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Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT). The sensor nodes(SNs) in WSNs are powered by batteries, making energy efficiency and network lifetime key issues in WSNs research. Cluster-routing algorithms are a focal point for addressing energy efficiency challenges. Selecting cluster heads (CHs) based on clustering algorithms can reduce the energy consumption of SNs and enhance overall network stability and sustainability. This paper introduces a method for selecting the number of clusters (\({N_C}\)) and CHs based on fuzzy clustering. The fuzzy C-means (FCM) clustering algorithm requires pre-setting the number of clusters, with no inclusion of CHs information in the output after running the algorithm. The number of clusters and selection of CHs were determined using the elbow rule and scoring criteria for CHs selection. The performance of the network under different monitoring areas is simulated and analyzed in this paper. Experiments demonstrate that the proposed algorithm outperforms existing algorithms in terms of network energy consumption and lifetime.

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“…Te performance of the clustering algorithm can be evaluated by external evaluation criteria and internal evaluation criteria [22][23][24]. Te internal evaluation criteria are calculated based on the dissimilarity of objects in clusters, and the dissimilarity measure of several algorithms is different, and accordingly, the internal evaluation results of diferent algorithms will not be on the same scale.…”

Section: Performance Evaluation Of the Improved K-prototypesmentioning

confidence: 99%

Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

Yan-fang

Chu

2023

Mathematical Problems in Engineering

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Elderly individuals with similar abilities are likely to have similar care needs. Abilities are the basis for analysing the need patterns of elderly individuals. Abilities of elderly individuals are both numerical and categorical; therefore, an improved K-prototypes cluster algorithm for mixed attributes was proposed to group elderly individuals based on abilities. In this algorithm, the dissimilarity measure of categorical attributes is designed to consider not only the difference between the object to be allocated and the prototype of the cluster but also the differ6ence between the object to be allocated and other objects in the cluster and can measure the difference in sequential values of ordinal categorical attributes. Experimental results show that the improved K-prototypes algorithm performs well for clustering datasets containing mixed attributes. Taking the CHARLS dataset as an example, distinct groups of elderly individuals based on the improved K-prototypes algorithm showed significant ability differences.

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A survey of fuzzy clustering validity evaluation methods

Cited by 30 publications

References 107 publications

Creation of a methodology for assessing the impact of the intellectual capital management mechanism on key indicators of sustainable development of an enterprise

Creation of a methodology for assessing the impact of the intellectual capital management mechanism on key indicators of sustainable development of an enterprise

Energy efficient clustering routing algorithm based on improved FCM

Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

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