Joint Distribution Center Location Problem for Restaurant Industry Based on Improved K-Means Algorithm With Penalty

Zhou, Yiren; Xie, Ruxin; Zhang, Tianhui; Holguin-Veras, Jose

doi:10.1109/access.2020.2975449

Cited by 19 publications

(8 citation statements)

References 59 publications

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“…erefore, by establishing behavioral patterns, discover inconsistencies between patterns and implement anomaly detection of user behavior [8]. Veeraiyan et al believe that currently, the shortcomings of user behavior anomaly detection methods are: insufficient ability to automatically process large amounts of data [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

Yang

Hao

Zhao

et al. 2022

Security and Communication Networks

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As an effective network protection method, computer user behavior anomaly detection can detect unknown attack behaviors. In order to detect user behavior anomalies more efficiently, the authors propose a computer user behavior anomaly detection model based on the K-means algorithm. According to the actual characteristics of single-user behavior, the algorithm uses sliding time window to define transactions and uses the first location strategy to mine behavior patterns. On this basis, the fault-tolerant mode is adopted to compare the current behavior mode with the normal behavior mode, and the anomaly detection results are obtained. Experiments show that using data mining technology, the association rules of user commands, and the mining of sequence patterns, it can effectively discover the user’s behavior pattern, and using sequence matching algorithms such as recursive correlation functions and calculating the similarity between the user’s historical pattern and the current pattern, it provides the possibility to accurately judge user behavior. The following conclusions are obtained through experiments: the model training time is short, the accuracy is high, and it has certain robustness.

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Section: Literature Reviewmentioning

confidence: 99%

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

Yang

Hao

Zhao

et al. 2022

Security and Communication Networks

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“…Information is the result of processing data that can be justified into a form that is beneficial to the recipient. Information systems can be described as a set of processes that process and present information in such a way that it benefits the recipient [15].…”

Section: Research Methods 21 Information Systemmentioning

confidence: 99%

K-means method for clustering learning classes

Indriyanti

Prehanto

Vitadiar³

2021

IJEECS

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<span>Learning class is a collection of several students in an educational institution. Every beginning of the school year the educational institution conducts a grouping class test. However, sometimes class grouping is not in accordance with the ability of students. For this reason, a system is needed to be able to see the ability of students according to the desired parameters. Determination of the weight of test scores is done using the K-Means method as a grouping method. Iteration or repetition process in the K-Means method is very important because the weight value is still very possible to change. Therefore, the repetition process is carried out to produce a value that does not change and is used to determine the ability level of students. The results of the class grouping test scores affect the ability of students. Application of K-Means method is used in building an information system grouping student admissions in an educational institution. Acceptance of students will be grouped into 3 groups of learning classes. The results of testing the system that applies K-Means method and based on data on the admission of prospective students from educational institutions have very high accuracy with an error rate of 0.074. </span>

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“…For K-means algorithm, the selection of K-values and initial cluster centers is critical to clustering results. To avoid local optimization, we use d γ to limit the distances between initial cluster centers to obtain more dispersed initial clustering centers [15],…”

Section: K-means Algorithmmentioning

confidence: 99%

Trajectory planning in UAV emergency networks with potential underlaying D2D communication based on K-means

Zhang

Shi

Feng

et al. 2021

J Wireless Com Network

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At present, unmanned aerial vehicles (UAVs) have been widely used in communication systems, and the fifth-generation wireless system (5G) has further promoted the vigorous development of them. The trajectory planning of UAV is an important factor that affects the timeliness and completion of missions, especially in scenarios such as emergency communications and post-disaster rescue. In this paper, we consider an emergency communication network where a UAV aims to achieve complete coverage of potential underlaying device-to-device (D2D) users. Trajectory planning issues are grouped into clustering and supplementary phases for optimization. Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed, respectively. In addition, in order to balance sum throughput with trajectory length, we present a joint evaluation index. Then relying on this index, a third trajectory optimization algorithm is further proposed. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

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Joint Distribution Center Location Problem for Restaurant Industry Based on Improved K-Means Algorithm With Penalty

Cited by 19 publications

References 59 publications

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

K-means method for clustering learning classes

Trajectory planning in UAV emergency networks with potential underlaying D2D communication based on K-means

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