A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph

Kim, Jeong-Hun; Choi, Jong-Hyeok; Yoo, Kwan‐Hee; Loh, Woong-Kee; Nasridinov, Aziz

doi:10.3390/electronics8101094

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…, b i represents the minimum average distance of the sample µ i within a cluster from all other clusters. For the evaluation of the performance of the proposed method (IDBSCAN), the results have been compared to those of the DBSCAN, OPTICS and PAPC-DBSCAN [36][37][38]. Among them, in DBSCAN, set ε = 0.5, MinPts = 4; in OPTICS, set ε = 0.5, MinPts = 4, ξ = 0.02; in PAPC-DBSCAN, set kpick = 0.15, kdrop = 0.15, α = 4, γ = 0.3, MinPts = 5; in IDBSCAN, set the number of wolves as 30 and the number of iterations as 300.…”

Section: Analysis Of Clustering Results For Painting Objectsmentioning

confidence: 99%

Ship Painting Process Design Based on IDBSACN-RF

Yuan

Niu

et al. 2021

Coatings

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The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive to scientific management of the painting process and effective control of painting cost. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Then, a painting object classification model is constructed based on the random forest (RF). Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. The results show that the performance of DBSCAN is significantly improved. Furthermore, the accurate classification of painting objects by RF is achieved. The experiment proves that the dry film thickness qualification rate obtained by the painting process designed by IDBSCAN-RF is 92.3%, which meets the requirements of the performance standard of protective coatings (PSPC).

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Section: Analysis Of Clustering Results For Painting Objectsmentioning

confidence: 99%

Ship Painting Process Design Based on IDBSACN-RF

Yuan

Niu

et al. 2021

Coatings

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“…4) Density models, which search the data space for areas of varying density of data points in the data space. Examples of these models are DBSCAN and OPTIC (Kim et al, 2019).…”

Section: The Choice Of Clustersmentioning

confidence: 99%

Factors Influencing the Economic Behavior of the Food, Beverages and Tobacco Industry: A Case Study for Portuguese Enterprises

Murillo

Rocha²

2020

WJAE

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In today's world, it is increasingly important to conduct economic and financial analyzes of enterprises in all sectors to determine strengths, identify weaknesses and adopt strategies that allow them to be at the highest competitive level. In particular, the food sector plays an essential role in the economy of any country, representing a significant contribution to gross domestic product, total employment, and disposable income of households. In this work, we adopt a methodology for measuring efficiency based on the multidirectional efficiency analysis and other mathematical techniques (the calculation of the normal distribution intersection coefficient (NC value), analysis of clusters and principal components, and model fitting) in order to examine the factors that influence the performance of Portuguese enterprises in the food, beverages and tobacco industry for the period of 2006-2013. The results show a characterization of the financial structure of the sector and diagnosis through indexes that identify the strategic positioning of the enterprises in terms of efficiency scores. In addition, we also show that an analysis of the variables that must be approached differently to obtain better results regarding economic performance. Although there is an increase in credit with the acquisition of long-term debts, there is no evidence that this implies the ability of enterprises to grow faster, which affects profitability.

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Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications

et al. 2022

Energies

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In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry.

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A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph

Cited by 3 publications

References 27 publications

Ship Painting Process Design Based on IDBSACN-RF

Ship Painting Process Design Based on IDBSACN-RF

Factors Influencing the Economic Behavior of the Food, Beverages and Tobacco Industry: A Case Study for Portuguese Enterprises

Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications

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