An Improved K-Means Algorithm Based on Evidence Distance

Zhu, Ailin; Zhong, Hua; Tang, Yongchuan; Miao, Lingwei

doi:10.3390/e23111550

Cited by 13 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, H. Du et al (2018) used the improved density peak algorithm (DPC) to select the initial clustering center. Some scholars have replaced the traditional Euclidean distance with other distances to calculate the space between sample points and have demonstrated through experiments that the enhanced k-means algorithm has a more acceptable clustering result and intersection than the traditional one (Zhu et al, 2021). Although scholars have improved the traditional Kmeans algorithm from different angles, most improvements are limited to the random selection of initial cluster centers and do not fundamentally solve the problem of getting stuck in locally optimal solutions.…”

Section: Research On the Evaluation Methods Of Digital Economy Develo...mentioning

confidence: 99%

Research on the Construction of Digital Economy Index System Based on K-means-SA Algorithm

Su,

Dong,

et al. 2023

Sage Open

View full text Add to dashboard Cite

The digital economy is developing rapidly worldwide, which is of great strategic significance in leading and driving the process of high-quality and high-efficiency development of the economy. It has gradually become a new engine to promote China’s economic growth. Therefore, it is vital to establish a digital economy indicator system. In this context, according to the connotation of digital economy, comprehensively consider the four dimensions of digital foundation, digital application, digital innovation and digital benefit, and build a full-scale digital economy indicator system. However, some previous literature proposed operating the K-means clustering algorithm in optimizing the index system. The algorithm, susceptible to the initial selection of cluster center, generates completely different clustering results with other random seed points and is thus unsuitable for optimizing the index system. Based on this, the advantages of GRA, SA, and K-means algorithms are combined to propose a K-means-SA algorithm that can obtain the global optimal solution. Then, by combining the K-means SA algorithm and the rough set algorithm, the constructed index system is further optimized. Ultimately, it establishes a set of all-round, multi-system and multi-dimensional digital economy index systems, which is of great reference significance for formulating relevant policies, provides scientific index support for the subsequent extension of digital economy theory, and promotes the benign growth of the worldwide economy.

show abstract

Section: Research On the Evaluation Methods Of Digital Economy Develo...mentioning

confidence: 99%

Research on the Construction of Digital Economy Index System Based on K-means-SA Algorithm

Su,

Dong,

et al. 2023

Sage Open

View full text Add to dashboard Cite

show abstract

“…Traditional k-means implantations use the Euclidean distance to find the distances between the points [28]. However, [29] opted to use the evidence distance, which can deal with uncertainty. Instead of using the Euclidean distance, the method utilizes the evidence distance, resulting in higher accuracy and a reduced number of iterations.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

Alzakan,

Almousa,

Almarzoqi

et al. 2024

IJACSA

View full text Add to dashboard Cite

As the volume and complexity of data continue to grow exponentially, finding efficient and accurate clustering algorithms has become crucial for many applications. Kmeans clustering is a widely used unsupervised machine learning technique for data analysis and pattern recognition. Despite its popularity, k-means suffers from certain limitations, such as sensitivity to initial conditions, difficulty in determining the optimal number of clusters, and the potential for misclassification. This research paper proposes an enhanced approach for improving the accuracy and performance of the k-means clustering algorithm by incorporating post-processing techniques using a gradient boosting algorithm. The proposed method comprises training the gradient boosting model on the labeled training set, i.e., the samples with correct cluster assignments obtained from the kmeans algorithm, to predict the correct cluster assignments for the misclassified samples in the testing set. This results in refined cluster assignments for the testing set. The k-means algorithm is only used initially to cluster the data and obtain initial cluster assignments. The effectiveness of the proposed approach is validated through experiments on several benchmark datasets, and the results show a significant improvement in clustering accuracy and robustness compared to the standard k-means algorithm. The proposed approach has the potential to enhance the performance of k-means in various real-world applications and domains.

show abstract

“…In this study, RD was predicted using DSH data collected from a group of HNC patients who had received IMPT. Unsupervised K-means clustering machine learning (ML) algorithm [ 21 ] was developed to group patients according to the likelihood that they will develop high, medium, or low grades of RD. In an attempt to reduce RD, a new IMPT optimization technique was developed that incorporates predictive model findings.…”

Section: Introductionmentioning

confidence: 99%

IMPT of head and neck cancer: unsupervised machine learning treatment planning strategy for reducing radiation dermatitis

et al. 2023

View full text Add to dashboard Cite

Radiation dermatitis is a major concern in intensity modulated proton therapy (IMPT) for head and neck cancer (HNC) despite its demonstrated superiority over contemporary photon radiotherapy. In this study, dose surface histogram data extracted from forty-four patients of HNC treated with IMPT was used to predict the normal tissue complication probability (NTCP) of skin. Grades of NTCP-skin were clustered using the K-means clustering unsupervised machine learning (ML) algorithm. A new skin-sparing IMPT (IMPT-SS) planning strategy was developed with three major changes and prospectively implemented in twenty HNC patients. Across skin surfaces exposed from 10 (S10) to 70 (S70) GyRBE, the skin's NTCP demonstrated the strongest associations with S50 and S40 GyRBE (0.95 and 0.94). The increase in the NTCP of skin per unit GyRBE is 0.568 for skin exposed to 50 GyRBE as compared to 0.418 for 40 GyRBE. Three distinct clusters were formed, with 41% of patients in G1, 32% in G2, and 27% in G3. The average (± SD) generalised equivalent uniform dose for G1, G2, and G3 clusters was 26.54 ± 6.75, 38.73 ± 1.80, and 45.67 ± 2.20 GyRBE. The corresponding NTCP (%) were 4.97 ± 5.12, 48.12 ± 12.72 and 87.28 ± 7.73 respectively. In comparison to IMPT, new IMPT-SS plans significantly (P < 0.01) reduced SX GyRBE, gEUD, and associated NTCP-skin while maintaining identical dose volume indices for target and other organs at risk. The mean NTCP-skin value for IMPT-SS was 34% lower than that of IMPT. The dose to skin in patients treated prospectively for HNC was reduced by including gEUD for an acceptable radiation dermatitis determined from the local patient population using an unsupervised MLA in the spot map optimization of a new IMPT planning technique. However, the clinical finding of acute skin toxicity must also be related to the observed reduction in skin dose.

show abstract

An Improved K-Means Algorithm Based on Evidence Distance

Cited by 13 publications

References 45 publications

Research on the Construction of Digital Economy Index System Based on K-means-SA Algorithm

Research on the Construction of Digital Economy Index System Based on K-means-SA Algorithm

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

IMPT of head and neck cancer: unsupervised machine learning treatment planning strategy for reducing radiation dermatitis

Contact Info

Product

Resources

About