Centroid Update Approach to K-Means Clustering

Borlea, Ioan-Daniel; Precup, Radu‐Emil; Drăgan, Florin; Borlea, Alexandra-Bianca

doi:10.4316/aece.2017.04001

Cited by 21 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e objective function is required to be consistent with the error sum of the square sum of the search direction during the search operation. K-means clustering algorithm is a new scientific and effective bank loan risk management analysis algorithm, which mainly performs quantitative analysis [20]. When dealing with cubes, the size reduction technology can be used as the dimension of the two-dimensional transformation, and the dimensionality reduction actually uses some means to process the higher-dimensional data into the lower-dimensional data, and at the same time, the similarity between the data and the data before processing is consistent with the most basic data.…”

Section: Similarity Measurementioning

confidence: 99%

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Zhu

Liu

2021

Complexity

View full text Add to dashboard Cite

The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm.

show abstract

Section: Similarity Measurementioning

confidence: 99%

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

Zhu

Liu

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…There are also studies that present algorithm developments for two stages [6], such as the study that presented various methods for improving the initialisation and classification stages through cluster information and their DPs in the previous iteration [25]. The article reviewed by [26] proposed a heuristic algorithm which accelerates convergence by predicting the centroid locations according to statistical information from previous iterations. The other recent approaches for improving the K‐means algorithm use different distance measurements than the Euclidean measurement [27–29].…”

Section: Related Workmentioning

confidence: 99%

An improved K‐means algorithm for big data

Moodi¹,

Saadatfar

2021

IET Software

View full text Add to dashboard Cite

An improved version of K-means clustering algorithm that can be applied to big data through lower processing loads with acceptable precision rates is presented here. In this method, the distances from one point to its two nearest centroids were used along with their variations in the last two iterations. Points with an equidistance threshold greater than the equidistance index were eliminated from the distance calculations and were stabilised in the cluster. Although these points are compared with the research indexcluster radius-again in the algorithm iteration, the excluded points are again included in the calculations if their distances from the stabilised cluster centroid are longer than the cluster radius. This can improve the clustering quality. Computerised tests as well as synthetic and real samples show that this method is able to improve the clustering quality by up to 41.85% in the best-case scenario. According to the findings, the proposed method is very beneficial to big data.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Applied the k-means cluster analysis with the application of data mining techniques. K-means cluster is described, among others, in the books: L. Kaufman, P. Rousseeuw [145], and A. Kassambara [146], and in papers [147][148][149]. Clustering is a process of partitioning a set of data objects from one set into multiple classes.…”

Section: Data Sources and Study Designmentioning

confidence: 99%

Between Poverty and Energy Satisfaction in Polish Households Run by People Aged 60 and Older

Piekut¹

2021

Energies

View full text Add to dashboard Cite

The household sector contributes significantly to a country’s energy consumption. Energy carrier expenses are the highest expenditures in Polish household budgets. Households run by individuals aged 60 and older are heavily burdened with energy expenditures. The scientific aim of the research is to present and assess housing conditions, with particular emphasis on energy poverty in households run by individuals aged 60 and older. Multivariate statistical analyses were used to conduct the research objectives (cluster methods, variance methods, regression methods). This paper identifies a new index—one that has been applied to the situation in Poland. Households that consist of elderly people are strongly diversified in terms of housing conditions (including energy conditions). There are concerns that some households are not able to access energy services that are required to satisfy basic human needs, particularly individuals with low levels of education, living on social benefits, with low disposable incomes, or living in the countryside. Households represented by men aged 60 and older have better energy supply than households run by women. The older the individual representing the household, the greater the likelihood that his/her energy service needs are not met.

show abstract

Centroid Update Approach to K-Means Clustering

Cited by 21 publications

References 19 publications

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

[Retracted] Early Warning of Financial Risk Based on K‐Means Clustering Algorithm

An improved K‐means algorithm for big data

Between Poverty and Energy Satisfaction in Polish Households Run by People Aged 60 and Older

Contact Info

Product

Resources

About