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Improved k-means clustering algorithm 认领 引用 被引量：26
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作者 夏士雄 李文超 +2 位作者 周勇 张磊 牛强 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期435-438,共4页
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a... In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower. 展开更多
关键词 clustering k-means algorithm silhouette coefficient
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Hierarchical hesitant fuzzy K-means clustering algorithm 认领 引用 被引量：30
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作者 CHEN Na XU Ze-shui XIA Mei-mei 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2014年第1期1-17,共17页
Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets ar... Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm. 展开更多
关键词 90B50 68T10 62H30 Hesitant fuzzy set hierarchical clustering K-means clustering intuitionisitc fuzzy set
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Investigation of the J-TEXT plasma events by k-means clustering algorithm 认领 引用 被引量：1
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作者 Jianchao LI Xiaoqing ZHANG +11 位作者 Yu ZHANG Abba Alhaji BALA Huiping LIU Guohong ZHOU Nengchao WANG Da LI Zhongyong CHEN Zhoujun YANG Zhipeng CHEN Jiaolong DONG Yonghua DING the J-TEXT Team 《Plasma Science and Technology》 SCIE EI CAS CSCD 2023年第8期38-43,共6页
Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,th... Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data. 展开更多
关键词 k-means cluster analysis plasma event machine learning
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Equivalent Modeling with Passive Filter Parameter Clustering for Photovoltaic Power Stations Based on a Particle Swarm Optimization K-Means Algorithm 认领 引用
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作者 Binjiang Hu Yihua Zhu +3 位作者 Liang Tu Zun Ma Xian Meng Kewei Xu 《Energy Engineering》 EI 2026年第1期431-459,共29页
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl... This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research. 展开更多
关键词 Photovoltaic power station multi-machine equivalentmodeling particle swarmoptimization K-means clustering algorithm
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An efficient enhanced k-means clustering algorithm 认领 引用 被引量：35
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作者 FAHIM A.M SALEM A.M +1 位作者 TORKEY F.A RAMADAN M.A 《Journal of Zhejiang University-SCIENCE A》 EI CAS 2006年第10期1626-1633,共8页
In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared dista... In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation. 展开更多
关键词 Clustering algorithms Cluster analysis k-means algorithm Data analysis
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Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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Fuzzy k-Means Clustering-Based Machine Learning Models for LFO Damping in Electric Power System Networks 认领 引用 被引量：1
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作者 Md Shafiullah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期803-830,共28页
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous... Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions. 展开更多
关键词 Fuzzy k-means clustering grey wolf optimizer group method of data handling long short-term memory low-frequency oscillation power system stabilizer single machine infinite bus stability unified power flow controller
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Visual field prediction using K-means clustering in patients with primary open angle glaucoma 认领 引用
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作者 Junyoung Lee Jihun Kim +5 位作者 Hwayoung Kim Sangwoo Moon EunAh Kim Sanghun Jeong Hojin Yang Jiwoong Lee 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第1期63-68,共6页
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to... AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data. 展开更多
关键词 K-means clustering hierarchical ordered partitioning and collapsing hybrid pointwise linear regression visual field prediction
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Using 2D electrical resistivity imaging and borehole data to estimate N₆₀-value of soils with k-means clustering for subsurface geomaterials categorization 认领 引用
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作者 Ratchadakorn Chumkhiao Sukit Yindeesuk +1 位作者 Kuo Chieh Chao Shinya Inazumi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期3218-3233,共16页
Geotechnical information forms the cornerstone of geotechnical engineering,playing a vital role in the design,construction,protection,and mitigation of structures by providing critical insights into geomaterials and s... Geotechnical information forms the cornerstone of geotechnical engineering,playing a vital role in the design,construction,protection,and mitigation of structures by providing critical insights into geomaterials and subsurface characteristics.Traditionally,drilling has been the primary method for acquiring subsurface data.Nevertheless,in recent decades,non-intrusive methods such as electrical resistivity surveys,complemented by machine learning(ML)techniques,have emerged as effective tools for comprehensive geotechnical site investigations.This study aims to integrate two-dimensional(2D)electrical resistivity imaging(ERI)with the standard penetration test(SPT)to categorize geomaterials employing k-means clustering analysis(KMCA),facilitating a better understanding of subsurface characteristics.A power equation is developed by correlating soil resistivity with N₆₀-value along a specific 2D line,supported by borehole data.This equation is subsequently applied to derive the estimate of N₆₀-value in areas of the slope where SPT data could not be directly obtained.KMCA proves to be a robust and versatile tool,enabling the classification of geomaterials into three primary clusters corresponding to different competency levels based on SPT measurements.These clusters include low-competency materials(loose sand),moderate-competency materials(medium-stiff clay),and high-competency materials(stiff-hard clay).The application of the power equation successfully estimates the N₆₀-value in areas lacking borehole data,achieving a high level of accuracy with a coefficient of determination(R²)of 0.9467,a mean absolute error(MAE)of 3.94,and a root mean squared error(RMSE)of 5.21.Consequently,this approach demonstrates the potential of integrating geophysical surveys with ML techniques to enhance subsurface characterization and extend the reach of traditional geotechnical methods. 展开更多
关键词 Electrical resistivity imaging(ERI) Estimated N₆₀-value k-means clustering analysis(KMCA) Machine learning(ML) Standard penetration test(SPT) Subsurface characterization
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Research on Color Calculation and Digital Reproduction of Foshan Woodblock New Year Paintings Based on K-means Clustering and HSV Models 认领 引用
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作者 LI Hong-xin CHEN Jia-jun +1 位作者 ZHANG Ya XIONG Qiang 《印刷与数字媒体技术研究》 CAS 北大核心 2026年第2期31-44,共14页
Currently,investigations of the color attributes of Foshan Woodblock New Year Paintings are limited to qualitative descriptions,devoid of objective quantitative data backing.Their color solutions fail to satisfy the d... Currently,investigations of the color attributes of Foshan Woodblock New Year Paintings are limited to qualitative descriptions,devoid of objective quantitative data backing.Their color solutions fail to satisfy the distribution and creative requirements of the digital era.This study intends to utilize K-means clustering to calculate and analyze the color properties of Foshan Woodblock New Year Paintings.Incorporating contemporary display standards,this study examines color palettes consistent with traditional New Year Paintings culture.Firstly,a dataset consisting of 127 photos categorized into 6 principal types of Foshan Woodblock New Year Paintings was assembled.Images were subjected to pre-processing,which included color correction and noise reduction.Then,following the conversion of data from RGB to HSV color space to extract pixel vectors,the K-means algorithm was implemented with a specified K value for clustering.Primary colors were derived from both the comprehensive information and specific groups to create color schemes.Ultimately,in light of contemporary digital display attributes,the S and V values of cluster centers were moderately modified while maintaining conventional color characteristics to augment visual impact in digital distribution.The clustering results showed that Foshan Woodblock New Year Paintings display attributes of high saturation and pronounced value contrast,with prominent colors primarily consisting of high-saturation reds.This resulted in one comprehensive color scheme and six categorized schemes.This study offers methodological assistance for the quantitative analysis of New Year painting colors,providing practical recommendations for the innovative distribution of traditional New Year Paintings in contemporary media,and is crucial for the preservation and transmission of intangible cultural heritage. 展开更多
关键词 Color clustering K-means Foshan Woodblock New Year Paintings HSV color space Digital communication
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Integrated diagnosis of abnormal energy consumption in converter steelmaking using GWO-SVM-K-means algorithms 认领 引用
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作者 Fei-Xiang Dai Xiang-Jun Bao +2 位作者 Lu Zhang Xiao-Jing Yang Guang Chen 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第1期458-468,共11页
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ... To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency. 展开更多
关键词 Converter smelting process Abnormal energy diagnosis Gray wolf optimization algorithm Support vector machine K-means clustering algorithm
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM 认领 引用 被引量：14
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作者 Faquan Yang Ling Yang +3 位作者 Dong Wang Peihan Qi Haiyan Wang 无 《China Communications》 SCIE CSCD 2018年第12期55-63,共9页
For the existing support vector machine,when recognizing more questions,the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged.The characteristic parameter of the sign... For the existing support vector machine,when recognizing more questions,the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged.The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm,support vector machine is trained by grading algorithm so as to enhance the rate of convergence,improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper.Simulation results show that the average recognition rate based on this algorithm is enhanced over 30%compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR.The average recognition rate can reach 90%when the SNR is 5 dB,and the method is easy to be achieved so that it has broad application prospect in the modulating recognition. 展开更多
关键词 clustering algorithm feature extraction grading algorithm support vector machine modulation recognition
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Improvement and Parallelism of k-Means Clustering Algorithm 认领 引用 被引量：3
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作者 田金兰 朱林 +1 位作者 张素琴 刘璐 《Tsinghua Science and Technology》 EI CAS 2005年第3期277-281,共5页
The k-means clustering algorithm is one of the most commonly used algorithms for clustering analysis. The traditional k-means algorithm is, however, inefficient while working on large numbers of data sets and improvin... The k-means clustering algorithm is one of the most commonly used algorithms for clustering analysis. The traditional k-means algorithm is, however, inefficient while working on large numbers of data sets and improving the algorithm efficiency remains a problem. This paper focuses on the efficiency issues of cluster algorithms. A refined initial cluster centers method is designed to reduce the number of iterative procedures in the algorithm. A parallel k-means algorithm is also studied for the problem of the operation limitation of a single processor machine when given huge data sets. The analytical results demonstrate that these improvements can greatly enhance the efficiency of the k-means algorithm, i.e., allow the grouping of a large number of data sets more accurately and more quickly. The analysis has theoretical and practical importance for work on the improvement and parallelism of cluster algorithms. 展开更多
关键词 data mining cluster analysis k-means algorithm parallelism
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A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm 认领 引用 被引量：4
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作者 Xinliang Tang Man Zhang +3 位作者 Pingping Yu Wei Liu Ning Cao Yunfeng Xu 《Computers, Materials & Continua》 SCIE EI 2020年第9期1725-1739,共15页
In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy ... In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life. 展开更多
关键词 WSN node energy consumption nonuniform clustering routing algorithm
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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation 认领 引用 被引量：1
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作者 Mona Jamjoom Ahmed Elhadad +1 位作者 Hussein Abulkasim Safia Abbas 《Computers, Materials & Continua》 SCIE EI 2023年第7期367-382,共16页
Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease ... Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy. 展开更多
关键词 SVM machine learning GLCM algorithm K-means clustering LBP
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Similarity matrix-based K-means algorithm for text clustering 认领 引用 被引量：2
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作者 曹奇敏 郭巧 吴向华 《Journal of Beijing Institute of Technology》 EI CAS 2015年第4期566-572,共7页
K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo... K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable. 展开更多
关键词 text clustering K-means algorithm similarity matrix F-measure
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A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio 认领 引用 被引量：5
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作者 LEI Ke-jun TAN Yang-hong +1 位作者 YANG Xi WANG Han-rui 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第10期2451-2461,共11页
In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorith... In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method. 展开更多
关键词 cognitive radio(CR) blind multiband spectrum sensing(BMSS) K-means clustering(KMC) occupied subband set(OSS) idle subband set(ISS) information theoretic criteria(ITC) noise uncertainty
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An accelerated K-means clustering algorithm using selection and erasure rules 认领 引用 被引量：9
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作者 Suiang-Shyan LEE Ja-Chen LIN 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2012年第10期761-768,共8页
The K-means method is a well-known clustering algorithm with an extensive range of applications,such as biological classification,disease analysis,data mining,and image compression.However,the plain K-means method is ... The K-means method is a well-known clustering algorithm with an extensive range of applications,such as biological classification,disease analysis,data mining,and image compression.However,the plain K-means method is not fast when the number of clusters or the number of data points becomes large.A modified K-means algorithm was presented by Fahim et al.(2006).The modified algorithm produced clusters whose mean square error was very similar to that of the plain K-means,but the execution time was shorter.In this study,we try to further increase its speed.There are two rules in our method:a selection rule,used to acquire a good candidate as the initial center to be checked,and an erasure rule,used to delete one or many unqualified centers each time a specified condition is satisfied.Our clustering results are identical to those of Fahim et al.(2006).However,our method further cuts computation time when the number of clusters increases.The mathematical reasoning used in our design is included. 展开更多
关键词 K-means clustering Acceleration Vector quantization Selection Erasure
结合蝙蝠算法和紧密度改进的三支K-means算法 认领 引用 被引量：1
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作者 孙清 叶军 +2 位作者 曾广财 宋苏洋 汪一心 《山东大学学报（理学版）》 CAS CSCD 北大核心 2026年第1期65-75,共11页
本文结合蝙蝠算法和紧密度改进三支K-means算法,利用黄金分割系数和种群平均位置优化蝙蝠算法,根据优化后的蝙蝠算法搜索初始聚类中心,提高三支K-means算法的稳定性。依据紧密度判断核心域和边界域的阈值,减少边界域样本数量,提高三支K-... 本文结合蝙蝠算法和紧密度改进三支K-means算法,利用黄金分割系数和种群平均位置优化蝙蝠算法,根据优化后的蝙蝠算法搜索初始聚类中心,提高三支K-means算法的稳定性。依据紧密度判断核心域和边界域的阈值,减少边界域样本数量,提高三支K-means算法的准确性。对比实验采用9个数据集与6种聚类算法,实验结果表明本文算法提升聚类性能,验证本文算法有效性和实用性。 展开更多
关键词 K-means聚类 蝙蝠算法 紧密度 K-means算法 三支决策
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