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26th International Conference on Parallel and Distributed Computing (Euro-Par)

作者： He, Guanlin Vialle, Stephane Baboulin, Marc Univ Paris Saclay CNRS Cent Supelec Lab Rech Informat F-91405 Orsay France Univ Paris Saclay CNRS Lab Rech Informat F-91405 Orsay France

ISBN: (纸本)9783030715939;9783030715922

means is a standard algorithm for clustering data. It constitutes generally the final step in a more complex chain of high quality spectral clustering. However this chain suffers from lack of scalability when addressing large datasets. This can be overcome by applying also the k-means algorithm as a pre-processing task to reduce the input data instances. We describe parallel optimization techniques for the k-means algorithm on CPU and GPU. Experimental results on synthetic dataset illustrate the numerical accuracy and performance of our implementations.

关键词： k-means algorithm Spectral clustering Heterogeneous CPU-GPU computing

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A Differential Privacy k-means algorithm for Improving Privacy Budget Allocation 22

A Differential Privacy K-Means Algorithm for Improving Priva...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Sen Liu Jianhua Liu Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

As a privacy protection method with strict mathematical definition, differential privacy has been widely used in various fields of data mining including clustering algorithm. However, the traditional differential privacy k-means algorithm is sensitive to the selection of initial value, and the allocation of privacy budget is relatively single, which reduces the availability of the algorithm. In order to further improve the availability of the differential privacy k-means algorithm, this paper proposes a privacy budget allocation method combining error analysis to optimize algorithm iteration times and merge clustering, and carries out theoretical analysis and experimental verification at the same time. The results show that the algorithm not only satisfies the definition of differential privacy, but also improves the availability of clustering effectively.

关键词： Data mining Differential privacy k-means algorithm Privacy protection

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Evaluating and enhancing the performance of the k-means clustering algorithm for annual coastal bed evolution applications

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OCEANOLOGIA 2024年第2期66卷 267-285页

作者： Papadimitriou, Andreas Tsoukala, Vasiliki Natl Tech Univ Athens Sch Civil Engn Lab Harbour Works 5 Heroon Polytech Str Zografos 15780 Greece

The prediction of the coastal bed evolution at an annual scale utilizing processbased models is usually a complex task requiring significant computational resources. To compensate for this, accelerating techniques aiming at reducing the amount of input parameters are often employed. In the framework of this research, a comprehensive evaluation of the capacity of the widely-used k-means clustering algorithm as a method to obtain representative wave conditions was undertaken. Various enhancements to the algorithm were examined in order to improve model results. The examined tests were implemented in the sandy coastline adjacent to the port of Rethymno, Greece, utilizing an annual dataset of wave characteristics using the model MIkE21 Coupled Model FM. Model performance evaluation was carried out for each test simulation by comparing results to a "brute force" one, containing the bed level changes induced from the annual time series of hourly changing offshore sea state wave characteristics, deeming the results very satisfactory. The best-performing configurations were found to be related to the implementation of a filtering methodology to eliminate low-energy sea states from the dataset. Employment of clustering algorithms utilizing "smart" configurations to improve their performance could become a valuable tool for engineers desiring to obtain An accurate representation of annual bed level evolution, while simultaneously reducing the required computational effort. (c) 2024 Institute of Oceanology of the Polish Academy of Sciences. Production and hosting by Elsevier B.V.

关键词： Wave input reduction Cluster analysis Morphological bed evolution k-means algorithm Numerical modelling

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Bit-Flipping Scheme Using k-means algorithm for Bit-Patterned Media Recording

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IEEE TRANSACTIONS ON MAGNETICS 2022年第8期58卷

作者： Jeong, Seongkwon Lee, Jaejin Soongsil Univ Sch Elect Engn Seoul 06978 South Korea

As the need for storing significant amounts of data has increased, an increased areal density (AD) of hard disk drives is required. Bit-patterned media recording (BPMR) with the ability to increase AD is regarded as one of the future magnetic storage systems. Reducing the distance between islands storing 1 bit can provide high AD, but it creates problems of intersymbol and intertrack interferences. In this study, we propose a bit-flipping scheme using the k-means algorithm for the BPMR system. In the proposed scheme, the k-means algorithm, which is an iterative clustering method for identifying similar samples and assigning them to clusters, is used to assign the samples that consist of the main and neighboring data to clusters. The predicted index from k-means is utilized to compare and flip the sign of the main data when the sign is different. The proposed scheme flips the sign of data that is predicted to be an error and improves the performance using the neighboring information.

关键词： Bit-flipping (BF) bit-patterned media recording (BPMR) k-means algorithm signal detection

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Applying improved k-means algorithm into official service vehicle networking environment and research

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SOFT COMPUTING 2020年第11期24卷 8355-8363页

作者： Meng, Xiangxi Lv, Jianghua Ma, Shilong Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

In order to improve the traffic efficiency of official vehicles in the traffic road network, a backpressure routing control strategy for multi-commodity flow (official traffic flow) using official vehicle network environmental data information is proposed. Firstly, the road network composed of official service vehicle-mounted wireless network nodes is used to collect information on road conditions and official service vehicles. In order to improve the real-time and forward-looking route control, an official service vehicle flow forecasting method is introduced to construct a virtual official service vehicle queue. A multi-commodity flow (official service vehicle flow) backpressure route method is proposed, and an official service vehicle control strategy is designed to improve the self-adaptive route of k-means algorithm. In addition, the weight of backpressure strategy is improved according to traffic pressure conditions, and the adaptability of backpressure route algorithm is improved by using optimized parameters. Finally, the simulation results show that the proposed method can effectively control traffic vehicles and improve traffic smoothness.

关键词： Vehicle network environment for official use Virtual official vehicles Multi-commodity flow k-means algorithm Traffic control

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k-means algorithm Description and the Application of Cluster Analysis in Heavy Truck Vehicle Fault 5

K-means Algorithm Description and the Application of Cluster...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： kang, Hao Chen, Youyi Zhao, Hailong Army Acad Armored Forces Beijing Peoples R China

ISBN: (纸本)9781728143231;9781728143224

This paper describes the k-means clustering algorithm and proposes the key problems and optimization methods used by the algorithm. The algorithm is used to analyze the fault data of heavy-truck vehicles in different driving environments, which can realize the information integration, exploratory classification and rule analysis of vehicle faults, and provide strong data support for the overall scientific evaluation and prediction of vehicles.

关键词： dynamic clustering k-means algorithm gaussian distribution educlidean distance exploratory classification

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Parallel Implementation of k-means algorithm on FPGA

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IEEE ACCESS 2020年 8卷 41071-41084页

作者： Dias, Leonardo A. Ferreira, Joao C. Fernandes, Marcelo A. C. Univ Fed Rio Grande do Norte Lab Machine Learning & Intelligent Instrumentat nPITI IMD BR-59078970 Natal RN Brazil Univ Porto INESC TEC P-4200465 Porto Portugal Univ Porto Fac Engn P-4200465 Porto Portugal Univ Fed Rio Grande do Norte Dept Comp Engn & Automat BR-59078970 Natal RN Brazil Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA

The k-means algorithm is widely used to find correlations between data in different application domains. However, given the massive amount of data stored, known as Big Data, the need for high-speed processing to analyze data has become even more critical, especially for real-time applications. A solution that has been adopted to increase the processing speed is the use of parallel implementations on FPGA, which has proved to be more efficient than sequential systems. Hence, this paper proposes a fully parallel implementation of the k-means algorithm on FPGA to optimize the system's processing time, thus enabling real-time applications. This proposal, unlike most implementations proposed in the literature, even parallel ones, do not have sequential steps, a limiting factor of processing speed. Results related to processing time (or throughput) and FPGA area occupancy (or hardware resources) were analyzed for different parameters, reaching performances higher than 53 millions of data points processed per second. Comparisons to the state of the art are also presented, showing speedups of more than over a partially serial implementation.

关键词： Parallel implementation FPGA k-means algorithm reconfigurable computing

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Research on Credit Rating of E-commerce Sellers based on k-means algorithm -- Taking Taobao Children's Clothing Store as an Example

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第10期8卷 230-236页

作者： Jingjing Hu

With the popularity of e-commerce, seller credit has become a decisive factor in transactions on e-commerce platforms. This article clarifies the current e-commerce sellers' credit score calculation rules and e-commerce sellers' credit grading methods. For the seller's e-commerce credit score, eight related indicators are selected: product quality, picture matching, reasonable pricing, service attitude, delivery speed, after-sales service, logistics speed, and packaging quality. Take *** children's clothing sellers as an example. We quantify the weights of the eight factors. Then, the k-means algorithm was used to grade the credit of the collected 48 stores on the Python platform.

关键词： E-commerce Sellers Credit Evaluation System k-means algorithm

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Analysis of athletes' stadium stress source based on improved layered k-means algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5905-5914页

作者： Gong, Chen Northeast Elect Power Univ Sch Phys Educ Jilin 132012 Jilin Peoples R China

Most of the research on stressors is in the medical field, and there are few analysis of athletes' stressors, so it can not provide reference for the analysis of athletes' stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes' stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known k-means algorithm with the layering algorithm to form a new improved layered k-means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical k-means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

关键词： k-means algorithm athlete stress source machine learning

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Recognition of Mobile Robot Navigation Path Based on k-means algorithm

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第8期34卷

作者： Du, Qifei Wang, Dahu Sha, Lin Tianjin Polytech Univ Natl Demonstrat Ctr Expt Engn Training Educ Tianjin Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Henan Peoples R China Tianjin Polytech Univ Sch Elect Engn & Automat Tianjin Peoples R China

With the rapid development of computer technology and electronics industry, computer processing capability and image processing technology have been greatly improved, making robots based on computer processing and image processing have entered a new development in the field of navigation path recognition research. As an indispensable carrier for intelligent manufacturing and industrial development, robots are expanding their applications. The key to the successful execution of the mobile robot is to move according to the planned path and to avoid obstacles autonomously. These two points depend on the validity and accuracy of navigation path identification. At present, research on mobile robot navigation path recognition mainly uses visual navigation as the main method, which uses visual sensors to simulate human eye functions, obtains relevant information from external environment images, and processes them to realize related functions that the system needs to complete. The two major problems in visual navigation are poor recognition ability and insufficient ability to resist light source interference. The main purpose of this paper is to improve the recognition ability of mobile robot navigation path and the ability to resist light source interference. It mainly uses the k-means algorithm for visual navigation research. By simulating the acquired image and the selected color space, the results show that the average time taken to complete a path identification method is 152 ins. Under different illumination environments, the information extraction rate of mobile robot navigation path can reach 90%, and the effect of strong light on navigation path recognition is effectively reduced under strong illumination environment. The results show that the recognition of the visual navigation path of a mobile robot using the k-means algorithm is more precise than the conventional method, and it takes less time to better meet the timeliness requirements of mobile robots.

关键词： k-means algorithm mobile robot visual navigation path recognition

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