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improved k-means clustering algorithm for Big Data Mining under Hadoop Parallel Framework

JOURNAL OF GRID COMPUTING 2020年第2期18卷 239-250页

作者： Lu, Weijia Nantong Univ Informat Ctr Affiliated Hosp Nantong Jiangsu Peoples R China

In order to improve the accuracy and efficiency of the clustering mining algorithm, this paper focuses on the clustering mining algorithm for large data. Firstly, the traditional clustering mining algorithm is improved to improve the accuracy, and then the improved clustering algorithm is parallelized to improve the efficiency. In order to improve the accuracy of clustering, an incremental k-means clustering algorithm based on density is proposed on the basis of k-means algorithm. Firstly, the density of data points is calculated, and each basic cluster is composed of the center points whose density is not less than the given threshold and the points within the density range. Then, the basic cluster is merged according to the distance between the two cluster centers. Finally, the points that are not divided into any cluster are divided into the clusters nearest to them. In order to improve the efficiency of the algorithm and reduce the time complexity of the algorithm, the distributed database was used to simulate the shared memory space and parallelize the algorithm on the Hadoop platform of cloud computing. The simulation results show that the clustering accuracy of the proposed algorithm is higher than that of the other two algorithms by more than 10%.

关键词： improved k-means clustering algorithm Big data mining Hadoop parallel framework Shared storage space Parallel computing Parallelization Distributed database

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Working condition recognition of grate cooler based on an improved k-means clustering algorithm 5

Working condition recognition of grate cooler based on an im...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Wang Shenghui Zhao Siyue Changchun Univ Technol Coll Elect & Elect Engn Changchun Peoples R China

ISBN: (纸本)9781665423144

The working condition control of grate cooler always affects the efficiency of the whole cement process. In order to solve the working condition of grate cooler too parameter dimension and strong coupling problem, and according to the actual production of collecting the data of grate cooler used principal component analysis combined with an improved k-means clustering algorithm, is used to identify the conditions of grate cooler, working condition of the formation of grate cooler model, through the calculation speed and stability analysis is made of the advantages of the new algorithm, the accuracy as an important parameter of working condition of the grate cooler model verified, it provides the model basis for the subsequent grate cooler automatic control.

关键词： principal components analysis improved k-means clustering algorithm Grate Cooler Condition recognition

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Research on Quantitative Method of Power Network Risk Assessment Based on improved k-means clustering algorithm

Research on Quantitative Method of Power Network Risk Assess...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Ren, Chong Sung, Zhenyu Li, Xiaoteng Fan, Guoqi Huo, Chao Cheng, Lin State Grid Corp China Northwest Branch Xian Peoples R China NARI Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China State Grid Jinhua Power Supply Co Jinhua Zhejiang Peoples R China Shanxi Elect Power Res Inst Xian Peoples R China

ISBN: (纸本)9781728167824

The risk assessment of the power grid is helpful to guide decision makers to take corresponding measures to improve the security and stability of the power grid operation. Existing risk assessment methods cannot comprehensively and accurately quantify grid risks. To this end, this paper proposes a quantification of grid risk assessment method based on an improved k-means clustering algorithm. The improved k-means clustering algorithm is used to classify key state quantities of the grid. The actual operating state quantity is matched with the classification situation to determine the risk of power grid abandonment and power restriction. Next, determine the best classification number and intra-class data of the data. According to the magnitude of the peak-valley difference, select the data of the photovoltaic and hydropower peak-valley difference and the small peak-valley difference when discarding the light, and verify the load probability of abandoning light by fitting the load data. In the load limit verification, select the load with large peak-to-valley difference and photovoltaic and hydropower data with small peak-to-valley difference, and perform load-limit verification by fitting the hydropower data. Finally, based on the actual data of the Northwest Power Grid, the quantitative comparison and analysis of the scheduling deviation between the fitted data and the actual data, and the regional power grid risk without probability improvement, verified the effectiveness and superiority of the method.

关键词： improved k-means clustering algorithm quantification of grid risk assessment Data fitting accurate evaluation

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Research on Quantitative Method of Power Network Risk Assessment Based on improved k-means clustering algorithm

Research on Quantitative Method of Power Network Risk Assess...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Chong Ren Zhenyu Sun Xiaoteng Li Guoqi Fan Chao Huo Lin Cheng Northwest Branch of State Grid Corporation of China Xi'an China NARI Group Corporation (State Grid Electric Power Research Institute) Nanjing China Shanxi Electric Power Research Institute Xi'an China State Grid Jinhua Power Supply Company Zhejiang China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

关键词： improved k-means clustering algorithm quantification of grid risk assessment Data fitting accurate evaluation Power grids data fitting Risk Assessment Risk Management clustering algorithms PHOTOVOLTAIC WATER POWER hydropower Quantitative Methods LOAD

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Malware recognition approach based on self-similarity and an improved clustering algorithm

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IET SOFTWARE 2022年第5期16卷 527-541页

作者： Chen, Jinfu Zhang, Chi Cai, Saihua Zhang, Zufa Liu, Lu Huang, Longxia Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 202013 Jiangsu Peoples R China Jiangsu Univ Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Jiangsu Peoples R China Univ Leicester Sch Comp & Math Sci Leicester Leics England

The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high false positive rate (FPR)) in some cases. In this paper, we initially use the k-means clustering algorithm to extract malware patterns under user to root attacks in network traffic. Since the traditional k-means algorithm needs to determine the number of clusters in advance and it is easily affected by the initial cluster centres, we propose an improved k-means clustering algorithm (NIkclustering algorithm) for cluster analysis. Furthermore, we propose the use of self-similarity and our improved clustering algorithm to recognise buffer overflow vulnerabilities for malware in network traffic. This motivates us to design and implement a recognition approach for buffer overflow vulnerabilities based on self-similarity and our improved clustering algorithm, called Reliable Self-Similarity with improved k-means clustering (RSS-Ikclustering). Extensive experiments conducted on two different datasets demonstrate that the RSS-Ikclustering can achieve much fewer false positives than other notable approaches while increasing accuracy. We further apply our RSS-Ikclustering approach on a public dataset (Center for Applied Internet Data Analysis), which also exhibited a high accuracy and low FPR of 96% and 1.5%, respectively.

关键词： pattern clustering improved clustering algorithm invasive software improved k-means clustering algorithm data analysis malware recognition approach cluster analysis cluster centres malware patterns NIkclustering algorithm network traffic traditional k-means algorithm

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A cost-minimized two-stage three-way dynamic consensus mechanism for social network-large scale group decision-making: Utilizing k-nearest neighbors for incomplete fuzzy preference relations

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Zhan, Jiaxin Cai, Mingjie Hunan Univ Sch Math Changsha 410082 Peoples R China Hunan Univ Shenzhen Res Inst Shenzhen 518000 Guangdong Peoples R China Hunan Key Lab Intelligent Decis Making Technol Eme Changsha 410082 Hunan Peoples R China

In the era of big data, large scale group decision-making (LSGDM) with social networks (SNs) (namely, SN-LSGDM) has become a hot topic in the field of decision science. Faced with the explosive growth of information, decision-makers (DMs) face immense challenges in processing and integrating vast amounts of data, often finding it difficult to fully comprehend all the information, leading to potentially incomplete expressions of their fuzzy preference relations (FPRs). This limitation in information processing not only affects the quality of decision-making but also increases the difficulty and cost of reaching a consensus. To overcome these challenges and enhance the efficiency and accuracy of decision-making, this paper designs a consensus model that minimizes adjustment costs in light of a dynamic trust network. Firstly, we introduce a measurement method based on k-nearest neighbor (kNN) information, which comprehensively considers the trust level of DMs and the similarity of preference relations, effectively filling in missing preference information and improving the completeness and accuracy of decision-making. In addition, an improved kmeans clustering algorithm is adopted, which takes into account the mutual influences between DMs and the cost of unit adjustment. On this basis, a two-stage minimum adjustment cost consensus reaching mechanism based on three-way decision (TWD) is proposed, using comprehensive adjustment priority as the criterion for division, to achieve feedback adjustment at the individual and subgroup levels, ensuring the coordination and consistency of the decision-making plan. At the same time, an optimization model is introduced to achieve cost minimization. Through detailed case studies and comparative analysis, the feasibility and superiority of this method in practical applications have been demonstrated.

关键词： k-nearest neighbor improved k-means clustering algorithm Three-way decision Incomplete information Large scale group decision-making

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A Superposition Assessment Method of Road Crash Risk and Congestion Risk: An Empirical Analysis

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第4期24卷 4262-4276页

作者： Cheng, Zeyang Zhang, Weihua Lu, Jian Yu, Bo Wang, Shiguang Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China Southeast Univ Jiangsu Key Lab Urban ITS Nanjing 211189 Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Nanjing 211189 Peoples R China

In road transport systems, various traffic risks in certain condition could produce joint actions, which increases the complexity of traffic risk assessment. Previous single risk assessment fails to reflect the superposition effect of multi-type traffic risks, so the result may underestimate or overrate the total risk strength of transport system. To address this problem, a novel risk assessment perspective that aims to evaluate the superposition effect of several traffic risks is studied. In this study, the risk quantification and standardization for single traffic risks is conducted first. Then a GARCH-VaR model is developed to explore the superposition impact of these single traffic risks. The GARCH-VaR model integrates the VaR theory and GARCH model, from which the superposition traffic risk is obtained by assigning every single traffic risk a reasonable weight. Finally, an improved k-means clustering algorithm is proposed to classify the superposition risk level. Empirical results demonstrate that the superposition risk of crash risk and congestion risk is lower than a single traffic risk in certain condition, which attributes to the weak interactions between various traffic risks. This finding illustrates the superposition risk does not necessarily go up with the increase of the risk category. Then the superposition risks are classified into high-risk level, moderate-risk level, and low-risk level, among which the classification accuracy of high-risk level is 92.85%-95.23%. The proposed method provides a theoretical reference for collaborative assessment of multi-type traffic risks, and the results could be potentially used in the comprehensive management of traffic risks.

关键词： Accidents Roads Real-time systems Risk management Computer crashes Predictive models Spatiotemporal phenomena Superposition assessment multi-type traffic risk GARCH-VaR model improved k-means clustering algorithm comprehensive management

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Evaluation of Distributed Photovoltaic Economic Access Capacity in Distribution Networks Considering Proper Photovoltaic Power Curtailment

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ENERGIES 2024年第17期17卷 4441页

作者： Hao, Wenbo Xiao, Weisong Yan, Qingyu Jia, Qingquan Hu, Benran Li, Pan State Grid Heilongjiang Elect Power Res Inst Harbin 150030 Heilongjiang Peoples R China Yanshan Univ Sch Elect Engn Key Lab Power Elect Energy Conservat & Drive Contr Qinhuangdao 066004 Peoples R China State Grid Heilongjiang Elect Power Co Ltd Harbin 150000 Peoples R China

The high proportion of distributed photovoltaic (DPV) access has changed the traditional distribution network structure and operation mode, posing a huge threat to the stable operation and economy of the distribution network. Aiming at a reasonable access capacity of DPV in the distribution network, this paper proposes an economic access capacity evaluation method for DPV in the distribution network considering proper PV power curtailment. Firstly, a method for generating typical joint light intensity and load power operation scenarios based on an improved k-means clustering algorithm is proposed, which provides comprehensive scenario support for the evaluation. Secondly, based on active and reactive power regulation, this paper proposes a DPV access capacity enhancement method to improve the DPV access capacity. Thirdly, considering proper PV power curtailment, an evaluation model of DPV economic access capacity in the distribution network is established to solve the maximum DPV economic access capacity in the distribution network. And aiming at the nonlinear problem in the model, the second-order cone relaxation method is employed to transform the model into the second-order cone programming model, so as to solve the objective function conveniently and efficiently. Finally, based on the improved IEEE 33-node distribution network analysis, the results show that the proposed method can be more comprehensive and effective in evaluating the DPV economic access capacity in the distribution network, and proper PV power curtailment can significantly increase the DPV economic access capacity in the distribution network.

关键词： distributed photovoltaic economic access capacity power curtailment improved k-means clustering algorithm opportunity constraints second-order cone relaxation

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Brain tumour segmentation and classification with reconstructed MRI using DCGAN

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 92卷

作者： Sandhiya, B. Raja, S. kanaga Suba Shruthi, k. kamala, S. Praveena Rachel Easwari Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol CSE Dept Tiruchirapalli India Easwari Engn Coll Dept Biomed & Engn Chennai India

The median lifespan of brain malignance diagnosed patients is poor, which indicates that just two out of ten persons identified with brain malignance will live lasting a minimum of a decade, resulting in numerous of individuals perish from brain malignance every year. Therefore, early diagnosis is crucial to save lives. Deep learning has drastically altered and enhanced methods for effective and highly accurate diagnosis, prediction, and recognition in recent years. Deep Neural Networks (DNN) are utilized to identify the malignance portion from MRI references. In order to provide a huge data set more affordably, our suggested method makes use of Deep Convolutional Generative Adversarial Network (DCGAN), a pre-processing approach that generates counterfeit pictures that deceive the discriminator into pretending they are real followed by other pre-processing techniques. Then, the separation was done by extracting different Magnetic Resonance Imaging (MRI) angles of the brain were taken into account, and several networks with variable nodes and weight ages were used with the help of an improved k-means clustering (kMC) algorithm. Using statistical and texture-based characteristics that are retrieved using the Gray-Level Co-occurrence Matrix (GLCM), the objective of this work's performance evaluation is to differentiate typical and unusual pixels. The model, which separates the pre-processed data into three forms-meningioma, glioma, pituitary, and a plain is trained using an enhanced faster Region-based Convolutional Neural Network (R-CNN). The faster R-CNN method obtains a classification precision of 96%, and sensitivity of 89.23% for Glioma, and a sensitivity of 96.28% for pituitary.

关键词： Brain Tumor Segmentation and Classification Deep Convolutional Generative Adversarial Network improved k-means clustering algorithm improved Region-based Convolutional Neural

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Diagnosis of the single phase-to-ground fault in distribution network based on feature extraction and transformation from the waveforms

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6079-6086页

作者： Shi, Fang Zhang, Linlin Zhang, Hengxu Xu, kai Vladimir, Terzija Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan Shandong Peoples R China State Grid Jinan Power Supply Co Jinan Shandong Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

The increasing permeation of the distributed generators in the power system brings great challenges for fault diagnosis, especially for the distribution networks with ungrounded neutral or grounded by Peterson coil as the fault current is limited and easily affected by the noises and interferences. A single phase-to-ground fault section identification method is proposed based on feature extraction of the synchronous waveforms and the calculation of the eigenvalues for the time-sequenced features. First, several fault features are defined and extracted from the synchronous current waveforms obtained by the fault recorders. Then, the topology related fault feature matrix is constructed using the time-series features obtained from different measurement sites, and the eigenvalues of the matrix are calculated based on the random matrix theory. Lastly, using the distribution characteristics of the eigenvalues, improved k-means clustering algorithm is utilised in classifying the fault cases and identifying the faulty sections. The effectiveness of the proposed scheme is verified by IEEE 34 nodes test system and a multi-feeder distribution network.

关键词： feature extraction power distribution faults fault diagnosis earthing distributed power generation fault location eigenvalues and eigenfunctions pattern clustering fault currents time series feature extraction distributed generators power system fault diagnosis Peterson coil fault current single phase-to-ground fault section identification method eigenvalues time-sequenced features fault features synchronous current waveforms fault recorders topology related fault feature matrix time-series features random matrix theory distribution characteristics fault cases multifeeder distribution network improved k-means clustering algorithm

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