Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

He, Hang; Liu, Wanggen; Zhao, Zhenhan; He, Shan; Zhang, Jinghui

doi:10.32604/csse.2022.027211

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…It separates observation objects of the sample with high similarity into one cluster and those with low similarity into different clusters, according to Tong et al (2023) [37]. There are many clustering methods [38,39]; however, the algorithms considered in the experiment are hard clustering algorithms. Observation objects in the set belong to only one cluster.…”

Section: -1-experimental Settingmentioning

confidence: 99%

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Lebedev,

Sukhoparov

2024

Emerg Sci J

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This paper presents a solution for building and implementing data processing models and experimentally evaluates new possibilities for improving ensemble methods based on multilevel data processing models. This study proposes a model to reduce the cost of retraining models when transforming data properties. The research objective is to improve the quality indicators of machine learning models when solving classification problems. The novelty is a method that uses a multilevel architecture of data processing models to determine the current data properties in segments at different levels and assign algorithms with the best quality indicators. This method differs from the known ones by using several model levels that analyze data properties and assign the best models to individual segments of data and training. The improvement consists of using unsupervised clustering of data samples. The resulting clusters are separate subsamples for assigning the best machine-learning models and algorithms. Experimental values of quality indicators for different classifiers on the whole sample and different segments were obtained. The findings show that unsupervised clustering using multilevel models can significantly improve the quality indicators of “weak” classifiers. The quality indicators of individual classifiers improve when the number of data clusters is increased to a certain threshold. The results obtained are applicable to classification when developing models and machine learning methods. The proposed method improved the classification quality indicators by 2–9% due to segmentation and the assignment of models with the best quality indicators in individual segments. Doi: 10.28991/ESJ-2024-08-01-025 Full Text: PDF

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Section: -1-experimental Settingmentioning

confidence: 99%

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Lebedev,

Sukhoparov

2024

Emerg Sci J

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“…The purpose of clustering is to divide the samples with high similarity into one cluster and the samples with low similarity into different clusters [17]. There are many clustering methods, such as DPC [18], DBSCAN [19], spectral clustering [20]. In addition to those aforementioned clustering algorithms, the non-iterative ANN can also be used to solve the clustering problem [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

Fan

Wang

et al. 2022

Mathematics

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The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.

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Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

Cited by 2 publications

References 11 publications

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

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