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Ak-means clustering algorithm

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Journal of the Royal Statistical Society: Series C (Applied Statistics) 1979年第1期28卷 100-108页

作者： J. A. Hartigan M. A. Wong Yale University New Haven Connecticut U.S.A.

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An efficient Genetic algorithm-based defensive method to mitigate multiple attacks in RPL-enabled IoT network

Internet of Things (The Netherlands)

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Internet of Things (The Netherlands) 2025年 32卷

作者： Mohapatra, Prajna Paramita R., Bala krishnan Priyadarsini, Madhukrishna Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering National Institute of Technology Chhattisgarh Raipur 492010 India

Internet of Things (IoT) is the interconnection of billions of devices over the Internet. It is an umbrella of various concepts, protocols, and technologies that are used to create numerous benefits in productivity and automation. Despite the benefits it provides, there are challenges such as high cost of IoT devices, time-constraints, and overuse of Internet protocols and technologies which attackers often take advantage. To address this, IoT networks must have secure routing protocols that can provide security to the network and also utilize the benefits of existing technologies. However the lack of infrastructure, dynamic topology changes, resource constraints, and unreliable links make even the best existing protocol “Routing Protocol for Low Power and Lossy Network (RPL)” to be vulnerable for various attacks. Besides the trust management, that ensures only the reliable and legitimate nodes to participate in routing decisions, is another critical aspect that many existing solutions fail to consider. Hence, in this research, we propose a novel secure routing technique “Genetic algorithm-based Trusted framEwork for RPL (GATE-RPL)” that supports multi-topology routing and provides security to various devices in the IoT network. To overcome the security issues, the proposed work:(i) provides a “dynamic trust management technique” that maximizes the trust of nodes, links, and routing performance using a combination of k-means clustering and extended Genetic algorithm;and subsequently (ii) finds a trusted routing path between every node in the network. The experimental results indicate an average of 0.012% packet loss, 10.5 Mbps throughput, and 99% accuracy in identifying trustworthy routing paths. © 2025 Elsevier B.V.

关键词： Genetic algorithm IoT k-means clustering algorithm RPL Trust calculation

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Acceleratedk-means clustering algorithm for colour image quantization

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The Journal of Photographic Science 2008年第1期56卷 29-40页

Thek-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major problem is that a great deal of computational cost is consumed. To accelerate thek-means clustering algorithm, two test conditions are employed in the proposed algorithm. From the experimental results, it is found that the proposed algorithm significantly cuts down the computational cost of thek-means clustering algorithm without incurring any extra distortion.

关键词： COLOUR IMAGE QUANTIZATION k-means clustering algorithm VECTOR QUANTIZATION LBG algorithm

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Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved clustering algorithm

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IEEE ACCESS 2024年 12卷 76634-76647页

作者： Zhang, Ning Zhu, Liang Heilongjiang Univ Technol Sch Elect & Informat Engn Jixi 158100 Peoples R China

With the application and development of new technologies such as the Internet of Things and big data in the energy field, smart grids have gradually become the main trend for the future development of the power system. However, frequent communication between various participants in the smart grid can lead to higher privacy leakage risks, and malicious attackers can cause economic losses and security accidents to the smart grid by injecting abnormal power data. To address the issues of data privacy protection and anomaly detection in smart grids, this study proposed a matrix completion based smart grid data privacy protection scheme and an unsupervised learning based smart grid anomaly data detection scheme. A matrix completion based smart grid data privacy protection scheme was proposed to address the issue of data privacy protection between different trust domains in the smart grid. This scheme repaired missing elements in the relevant matrix through matrix completion, and increased random perturbations by adding noise with the same statistical characteristics as the original data to achieve data privacy protection. To address the issue of missing power data labels and inaccurate principal component analysis caused by abnormal data, an improved k-means clustering algorithm was used to remove outliers and optimize the results, to achieve accurate detection of abnormal data. The experimental results showed that the proposed algorithm has an accuracy of 0.9, an F1 value of 0.7, and a Bayesian detection rate of 0.61, all of which are higher than other algorithms. The AUC value of the proposed method was 0.79, indicating that the scheme established in this chapter has certain advantages in detecting abnormal electricity consumption.

关键词： Smart grids Electricity Data privacy Protection clustering algorithms Security Quantum computing Internet of Things Matrix completion k-means clustering algorithm smart grid data privacy monitor

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Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment

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COMPUTERS IN BIOLOGY AND MEDICINE 2012年第2期42卷 213-221页

作者： Chen, You-Shyang Cheng, Ching-Hsue Lai, Chien-Jung Hsu, Cheng-Yi Syu, Han-Jhou Hwa Hsia Inst Technol Dept Informat Management New Taipei City 235 Taiwan Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu 640 Yunlin Taiwan Natl Chin Yi Univ Technol Dept Distribut Management Taichung 411 Taiwan Lins ENT Clin Lugang Township 505 Changhua County Taiwan

Identifying patients in a Target Customer Segment (TCS) is important to determine the demand for, and to appropriately allocate resources for, health care services. The purpose of this study is to propose a two-stage clustering-classification model through (1) initially integrating the RFM attribute and k-means algorithm for clustering the TCS patients and (2) then integrating the global discretization method and the rough set theory for classifying hospitalized departments and optimizing health care services. To assess the performance of the proposed model, a dataset was used from a representative hospital (termed Hospital-A) that was extracted from a database from an empirical study in Taiwan comprised of 183,947 samples that were characterized by 44 attributes during 2008. The proposed model was compared with three techniques, Decision Tree, Naive Bayes, and Multilayer Perceptron, and the empirical results showed significant promise of its accuracy. The generated knowledge-based rules provide useful information to maximize resource utilization and support the development of a strategy for decision-making in hospitals. From the findings, 75 patients in the TCS, three hospital departments, and specific diagnostic items were discovered in the data for Hospital-A. A potential determinant for gender differences was found, and the age attribute was not significant to the hospital departments. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Customer relationship management (CRM) Target customer segment (TCS) k-means clustering algorithm Rough set theory (RST) Recency-Frequency-Monetary (RFM) analysis model

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Multipath/NLOS Detection Based on k-means clustering for GNSS/INS Tightly Coupled System in Urban Areas

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MICROMACHINES 2022年第7期13卷 1128页

作者： Wang, Hao Pan, Shuguo Gao, Wang Xia, Yan Ma, Chun Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab Microinertial Instrument & Adv Nav Techno Nanjing 210096 Peoples R China Nanjing Tech Univ Sch Geomat Sci & Technol Nanjing 211800 Peoples R China

Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the k-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the k-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

关键词： GNSS INS tightly coupled system multipath NLOS detection k-means clustering algorithm urban areas

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Flexible DC distribution line protection principle based on distance metric of clustering centres

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第18期16卷 3661-3672页

作者： Dai, Zhihui Liu, Shanshan Chen, Siqi Zhao, Jian Wu, Tong North China Elect Power Univ Sch Elect Engn Baoding Hebei Peoples R China

Aiming at the issue of unsatisfactory selectivity of single-terminal protection, and poor speed and high requirements for data synchronization of double-terminal protection in flexible DC distribution networks, an artificial intelligence (AI)-based method is proposed to obtain the best clustering centres through unsupervised clustering analysis of historical data to realize the non-deterministic flexible DC distribution line protection principle. The method forms historical data samples by simulating different types of short-circuit faults at different locations of the line in advance, combining them with the actual faults, and collecting and processing the post-fault currents. The k-means clustering algorithm is then used to find the best clustering centres corresponding to different fault types, and fault identification and pole selection are realized by comparing the distance between real-time data and each clustering centre. The process relies merely on single-terminal current as the characteristic quantity, and it does not need complicated feature extraction and calculation. Thus, the cumbersome threshold setting in conventional current protections can be avoided. Finally, the case studies are carried out in PSCAD/EMTDC, and the results show that the proposed protection has good selectivity and rapidity, and the tolerance to fault resistance is improved compared with the conventional local-current-based protections.

关键词： short-circuit faults fault identification fault location EMTP fault currents pole selection pattern clustering fault diagnosis artificial intelligence-based method single-terminal protection k-means clustering algorithm power distribution protection fault resistance DC distribution networks power distribution faults data synchronization PSCAD-EMTDC simulation power system CAD cumbersome threshold setting DC distribution line protection principle power distribution lines unsupervised clustering analysis local-current-based protection

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Flexible resource allocation optimization model considering global k-means load clustering and renewable-energy consumption

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Clean Energy 2024年第5期8卷 129-143页

作者： Jie Jiao Puyu He Yuhong Zhang Jiyuan Zhang Zhuhan Long Hanjing Liu State Grid Sichuan Electric Power Company Economic Research Institute ChengduChina

Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy *** investment construction cost and operation cost of various flexible resources are different,and the adjustment ability is different in different ***,the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different *** this paper,the global k-means load clustering model is pro-posed and the 365-day net load is reduced to eight typical daily net loads by ***,a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is *** flexible resources involved include the flexible transformation of thermal power,hydropower,pumped storage,energy storage,and demand *** upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the *** results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable *** the abandonment rate of renewable energy decreases,the optimal capacity of pumped storage,electrochemical energy storage,and hydropower units *** the power-abandonment rate of renewable energy is 5%,the optimal allocation capacity of thermal power flexibility transformation,pumped storage,electrochemical energy storage,hydropower unit,and adjustable load in Province A is 5313,17090,5830,72113,and 4250 MW,*** the condition that the renewable-energy abandonment rate is 0,5%,and 10%respectively,the configured capacity of pumped storage is 20000,17090,and 14847 MW,respectively.

关键词： wind power photovoltaic power k-means clustering algorithm flexible resources complementary configuration operation optimization

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A New clustering-Based Approach for Target Tracking to Optimize Energy Consumption in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2020年第4期114卷 3337-3349页

作者： Hosseini, Rahim Mirvaziri, Hamid Shahid Bahonar Univ Comp Engn Dept Kerman Iran

The emergence of wireless sensor networks is counted among the most significant achievements of the late 20's. Nowadays, by the ever increasing utilization of new technologies, accessibility of distributed processing technologies and the rising demand for these technologies in regions unreachable or hard-to-reach for humans, their application is greatly expanded. In this study, the objective application is target tracking in protected zones. In this respect, tracking and accurate position reporting of targets in the defined time unit is the main objective. Nevertheless, one of the challenges in these networks is their short lifetime. This is due to the energy limitation of the power supply in each unit. The topic of target tracking in wireless sensor networks is of great importance because on one hand, for their lack of recharge ability, sensor nodes get depleted quite quickly, while on the other hand, losing the target is a highly undesirable event that may severely hamper the functionality of the network. Thus, we are searching for a solution that minimizes tracking errors. In this paper, we propose a strategy for target tracking in wireless sensor networks that is able to generate the least possible tracking error while consuming the minimum energy.

关键词： Target tracking k-means clustering algorithm Wireless sensor networks Energy optimization

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A Lightweight Mutual Privacy Preserving k-means clustering in Industrial IoT

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IEEE TRANSACTIONS ON NETWORk SCIENCE AND ENGINEERING 2024年第2期11卷 2138-2152页

作者： Hu, Chunqiang Liu, Jianshuo Xia, Hui Deng, Shaojiang Yu, Jiguo Chongqing Univ Sch Big Data & Software Engn Chongqing 400030 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao 266100 Peoples R China Qilu Univ Technol Big Data Inst Jinan 250316 Peoples R China

In many industrial Internet of Things application schemes, participants, such as IoT devices, often share information with each others for analysis and categorization purpose. Through the analysis and statistical processing of the information, some necessary information is exchanged, e.g., the attributes in manufacturing IoT devices' gathered data. In this context, many devices are necessarily trusted when sharing information, which raises concerns about data privacy leakage. As the number of IoT devices in the network increases, the scale of pairwise keys increases rapidly. Furthermore, the limitation of device's computing ability makes it hard to perform centralized computing. To address these issues, we propose a light weight k-means clustering scheme that performs clustering with high accuracy, maintaining at a low key management cost, and securing the private attributes of each participant or the intermediate variables. In our proposed scheme, we use aprivate optimal initial cluster center generation algorithm based on attribute weights, in order to achieve the better clustering quality. Secondly, we securely find the nearest clustering center for each participant. We dynamically split and merge clustering centers respectively, in order to meet the optimal clustering result. Last but not least, we calculate the clustering centers without revealing any private attributes about the participants' information during the process. The analysis indicates that our proposed scheme canresist collusion attacks and ensures that the cost of overall pair wisekey management and computation cost performed in data center are relatively low.

关键词： k-means clustering algorithm privacy preservation lightweight clustering algorithm homomorphic encryption

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