r-Reference points based k-means algorithm

Wang, Ching-Lin; Chan, Yung-Kuan; Chu, Shao-Wei; Yu, Shyr-Shen

doi:10.1016/j.ins.2022.07.166

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Next, the above steps are repeated until the objective function converges. The data are then finally clustered into K categories, and the objective function is shown in Equation (3) [34]:…”

Section: Subjective Evaluation Of Participantsmentioning

confidence: 99%

Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort

et al. 2023

Sustainability

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The tunnel is an important component of freeway operation safety, and its classification method is the foundation of a refined management of operation safety. At present, the impact of different categories of tunnels on driver safety, comfort, and driving behavior under naturalistic driving conditions is not clear, and there is a lack of classification methods for tunnels of different lengths in their operation stages. This paper was based on the driving workload, which effectively expresses the safety and comfort of drivers. In this context, naturalistic driving experiments in 13 freeways and 98 tunnels with 36 participants were carried out. The DDTW+K-Means++ algorithm, which is suitable for drivers’ driving workload time series data, was used for a clustering analysis of the tunnels. According to the length of the tunnel, the operation-stage tunnels were divided into three categories: short tunnels (<450 m), general tunnels (450~4000 m), and long tunnels (>4000 m). The length of the tunnel had a positive correlation with the drivers’ driving workload, while there was a negative correlation with the vehicle running speed, and the range of changes in the drivers’ driving workload and operation safety risks in general tunnels and long tunnels was higher than that in short tunnels. Road and environmental conditions are important factors affecting the driving workload. The entrance area, the exit area of tunnels, and the middle area of long tunnels are high-risk sections in the affected area of the tunnel. These research results are of great significance for the operation safety management of freeway tunnels.

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“…Next, the above steps are repeated until the objective function converges. The data are then finally clustered into K categories, and the objective function is shown in Equation (3) [34]:…”

Section: Subjective Evaluation Of Participantsmentioning

confidence: 99%

Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort

et al. 2023

Sustainability

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“…Kim & Kim, 2023). Algoritma K-means merupakan teknik pengelompokan sederhana dan efektif namun algoritma K-means memakan waktu (Wang et al, 2022). Sebagian besar data sering diurutkan ke kelompok yang sama antara dua iterasi berturut-turut, terutama pada tahap akhir dari fase iterasi dari algoritma K-means.…”

Section: Metode Penelitianunclassified

Pendekatan K-Means Clustering Metode Elbow Pada Analisis Motivasi Pengunjung Festival Halal JHF#2

Wicaksana,

Heksaputra,

Syah

et al. 2023

JIEI

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Research that explores intrapersonal elements as a motivation for consumer decisions to visit a regional halal festival using a computational technique is still very rare. Motivation is one of the intrapersonal components of consumers and becomes a vital construct in the formation of marketing strategies in the context of goods and services. In this study, the mental activity that is the focus of the discussion is the determination of the rational factors considered by visitors as elements that shape people's motivation to attend the Jogja Halal Fest #2, referring to the instrument developed by Crompton and McKay. The method used in this study is the K-means clustering approach, whose results provide identification in the form of clustering patterns of visitor motives at JHF that tend to converge on educational, recreational, and transactional reasons, supported by the findings of a cluster number of 3. This study stimulates the importance of discussing the other motives that have not been explored, such as the position of spirituality, so that they can be used as a consideration for building an effective marketing program, especially those related to targeting visitors in the future (segmentation) and its impact on increasing halal literacy.

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“…K-Means Clustering algorithm categorizes financial transaction data into three clusters based on the similarities, with randomly determined cluster centers representing low, medium, and high-value financial transaction. These divided data clusters are then recalculated using Benford Law equation [21], [22], as shown in Fig. 1.…”

Section: B K-means Clustering Algorithmmentioning

confidence: 99%

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Wiryadinata,

Sugiharto,

Tarno

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection. Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool. Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report. Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered. Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction. Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.

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r-Reference points based k-means algorithm

Cited by 7 publications

References 14 publications

Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort

Cluster Analysis of Freeway Tunnel Length Based on Naturalistic Driving Safety and Comfort

Pendekatan K-Means Clustering Metode Elbow Pada Analisis Motivasi Pengunjung Festival Halal JHF#2

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

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