A survey on theory and algorithms for bm$k$-means problems

Zhang, Dongmei; Li, Min; Dachuan, XU; Zhang, Zhenning

doi:10.1360/ssm-2019-0280

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…The algorithm is used widely due to its computational simplicity and high efficiency. Based on the similarity measurement criteria proposed above, the calculation steps are as follows [37]:…”

Section: Clustering Methods For Dam Deformation Monitoring Pointsmentioning

confidence: 99%

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Cui

et al. 2023

Mathematics

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To ensure the safety of concrete dams, a large number of monitoring instruments are embedded in the bodies and foundations of the dams. However, monitoring data are often missing due to failure of monitoring equipment, human error and other factors that cause difficulties in diagnosis of dam safety and failure to precisely predict their deformation. In this paper, a new method for imputing missing deformation data is proposed. First, since the traditional deformation increment speed distance index of the deformation similarity index does not take into account the fact that there is little change in deformations occurring in two consecutive days, the denominator of the index tends to be equal to zero. In this paper, an improved index for solving this problem is proposed. A combined weighting method for calculating the deformation similarity comprehensive index and the k-means clustering method is then proposed and used to classify deformation monitoring points. Subsequently, a panel data model that imputes different types of missing data is established. The method proposed in this paper can impute missing concrete dam deformation data more accurately; therefore, it can effectively solve the missing deformation monitoring data problem.

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Section: Clustering Methods For Dam Deformation Monitoring Pointsmentioning

confidence: 99%

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Cui

et al. 2023

Mathematics

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“…In order to identify the types of glass artifacts, the content of chemical substances in unknown glass artifacts is an important criterion for classification, and the unknown glass is classified by the significant differences in the chemical composition of different types of glass. Then a sensitivity analysis is performed by changing a parameter of the type of glass [6].…”

Section: Introductionmentioning

confidence: 99%

A study on the discrimination and identification of ancient glassware types based on cluster analysis

Zheng¹,

Sui²,

Zhang³

et al. 2023

HSET

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The classification and identification of ancient glass artifacts is complicated due to the exchange of internal elements, which can be influenced by a multiplicity of different factors, such as weathering, erosion, and so on. To classify and identify the types of glass artifacts, this paper uses cluster analysis (K-Means) to study the main chemical composition of high potassium glass and lead-barium glass. It is found that the chemical composition of different glass ( potassium oxide, barium oxide, lead oxide) and subclasses types are significantly different, and the content of lead oxide in lead-barium glass also has a large difference. Using the clustering centers obtained by cluster analysis (K-Means), the eight unknown types of glass were first determined visually and briefly based on the different proportions of potassium oxide, barium oxide, and lead oxide. As for accurate classification, the Euclidean distances between the unknown components and the sample centroids were calculated separately, and the shortest Euclidean distances were identified by comparison, and then categorized for identification.

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An Approximation Algorithm Based on Seeding Algorithm for Fuzzy k-Means Problem with Penalties

Liu

2022

J. Oper. Res. Soc. China

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A survey on theory and algorithms for bm$k$-means problems

Abstract: A survey on network node ranking algorithms: Representative methods, extensions, and applications SCIENCE CHINA Technological Sciences RECURSIVE ALGORITHMS THEORY OF RECURSIVE ALGORITHMS Ⅰ.

Cited by 4 publications

References 57 publications

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

A study on the discrimination and identification of ancient glassware types based on cluster analysis

An Approximation Algorithm Based on Seeding Algorithm for Fuzzy k-Means Problem with Penalties

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