检索结果-南通市图书馆

A combined algorithm of k-means and MTRL for multi-class classification

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2019年第5期30卷 875-885页

作者： XUE Mengfan HAN Lei PENG Dongliang School of Automation Hangzhou Dianzi University

The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification *** order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining k-means and multi-task relationship learning(MTRL).The method first uses the split method of One *** to disassemble the multi-class classification task into binary classification tasks.k-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training ***,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained *** the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary *** effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.

关键词： machine learning multi-class classification k-means multi-task relationship learning (MTRL) over-fitting

Investigation of the J-TEXT plasma events by k-means clustering algorithm

在线全文

学校读者我要写书评

暂无评论

Plasma Science and Technology 2023年第8期25卷 38-43页

作者：李建超张晓卿张昱 Abba Alhaji BALA 柳惠平周帼红王能超李达陈忠勇杨州军陈志鹏董蛟龙丁永华 the J-TEXT Team Hubei key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205People's Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Department of Physics Federal University DutseJigawa 720101Nigeria 不详

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 *** 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 the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT *** cluster analysis method is conducive to data markers of massive diagnostic data.

关键词： k-means cluster analysis plasma event machine learning

Partition of GB-InSAR deformation map based on dynamic time warping and k-means

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2022年第4期33卷 907-915页

作者： TIAN Weiming DU Lin DENG Yunkai DONG Xichao Radar Research Lab School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China key Laboratory of Electronic and Information Technology in Satellite Navigation(Beijing Institute of Technology) Ministry of EducationBeijing 100081China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Centre Chongqing 401120China Advanced Technology Research Institute Beijing Institute of TechnologyJi'nan 250300China

Ground-based interferometric synthetic aperture radar(GB-InSAR)can take deformation measurement with a high *** of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene *** partition methods rely on labelled datasets or single deformation feature,and they cannot be effectively utilized in GBInSAR *** paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping(DTW)and *** DTW similarities between a reference point and all the measurement points are calculated based on their time-series *** the DTW similarity and cumulative deformation are taken as two partition *** the k-means algorithm and the score based on multi evaluation indexes,a deformation map can be partitioned into an appropriate number of *** datasets of West Copper Mine are processed to validate the effectiveness of the proposed method,whose measurement points are divided into seven classes with a score of 0.3151.

关键词： ground-based interferometric synthetic aperture radar(GB-InSAR) deformation map partition dynamic time warping(DTW) k-means

Enabling Highly Efficient k-means Computations on the SW26010 Many-Core Processor of Sunway TaihuLight

在线全文

学校读者我要写书评

暂无评论

Journal of Computer Science & Technology 2019年第1期34卷 77-93页

作者： Min Li Chao Yang Qiao Sun Wen-Jing Ma Wen-Long Cao Yu-Long Ao Institute of Software Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Mathematical Sciences Peking UniversityBeijing 100871China Center for Data Science Peking UniversityBeijing 100871China peng Cheng Laboratory Shenzhen 518052China

With the advent of the big data era,the amounts of sampling data and the dimensions of data features are rapidly *** is highly desired to enable fast and efficient clustering of unlabeled samples based on feature similarities. As a fundamental primitive for data clustering,the k-means operation is receiving increasingly more attentions *** achieve high performance k-means computations on modern multi-core/many-core systems,we propose a matrix-based fused framework that can achieve high performance by conducting computations on a distance matrix and at the same time can improve the memory reuse through the fusion of the distance-matrix computation and the nearest centroids *** implement and optimize the parallel k-means algorithm on the SW26010 many-core processor,which is the major horsepower of Sunway *** particular,we design a task mapping strategy for load-balanced task distribution,a data sharing scheme to reduce the memory footprint and a register blocking strategy to increase the data *** techniques such as instruction reordering and double buffering are further applied to improve the sustained *** on block-size tuning and performance modeling are also *** show by experiments on both randomly generated and real-world datasets that our parallel implementation of k-means on SW26010 can sustain a double-precision performance of over 348.1 Gflops,which is 46.9% of the peak performance and 84%of the theoretical performance upper bound on a single core group,and can achieve a nearly ideal scalability to the whole SW26010 processor of four core *** comparisons with the previous state-of-the-art on both CPU and GPU are also provided to show the superiority of our optimized k-means kernel.

关键词： parallel k-means performance optimization SW26010 processor Sunway TaihuLight

Greedy Optimization for k-means-Based Consensus Clustering

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2018年第2期23卷 184-194页

作者： Xue Li Hongfu Liu School of Economics and Management Tsinghua University Beijing 100084 China. Department of Electrical and Computer Engineering Northeastern University Boston MI 02115 USA.

Consensus clustering aims to fuse several existing basic partitions into an integrated one; this has been widely recognized as a promising tool for multi-source and heterogeneous data clustering. Owing to robust and high-quality performance over traditional clustering methods, consensus clustering attracts much attention, and much efforts have been devoted to develop this field. In the literature, the k-means-based Consensus Clustering(kCC) transforms the consensus clustering problem into a classical k-means clustering with theoretical supports and shows the advantages over the state-of-the-art methods. Although kCC inherits the merits from k-means,it suffers from the initialization sensitivity. Moreover, the current consensus clustering framework separates the basic partition generation and fusion into two disconnected parts. To solve the above two challenges, a novel clustering algorithm, named Greedy optimization of k-means-based Consensus Clustering(GkCC) is *** by the well-known greedy k-means that aims to solve the sensitivity of k-means initialization, GkCC seamlessly combines greedy k-means and kCC together, achieves the merits inherited by GkCC and overcomes the drawbacks of the precursors. Moreover, a 59-sampling strategy is conducted to provide high-quality basic partitions and accelerate the algorithmic speed. Extensive experiments on 36 benchmark datasets demonstrate the significant advantages of GkCC over kCC and kCC++ in terms of the objective function values and standard deviations and external cluster validity.

关键词： k-means consensus clustering initialization greedy optimization

Long-term Traffic Volume Prediction Based on k-means Gaussian Interval Type-2 Fuzzy Sets

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1344-1351页

作者： Runmei Li Yinfeng Huang Jian Wang the School of Electronic and Information Engineer ing Beijing Jiaotong University

This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A k-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by k-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership *** the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined k-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

关键词： Gaussian interval type-2 fuzzy sets k-means clustering long-term prediction traffic volume traffic volume fluctuation range

A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and k-means

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2015年第4期24卷 694-701页

作者： TRAN Dang Cong WU Zhijian WANG Zelin DENG Changshou State key Laboratory of Software Engineering School of Computer Wuhan University Vietnam Academy of Science and Technology School of Information Science and Technology Jiujiang University

To improve the performance of k-means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and kmeans(EABCk). In EABCk, the original artificial bee colony algorithm(called ABC) is enhanced by a new mutation operation and guided by the global best solution(called EABC). Then, the best solution is updated by kmeans in each iteration for data clustering. In the experiments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCk on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCk outperform other comparative ABC variants and data clustering algorithms, respectively.

关键词： Data clustering k-means Artificial bee colony Mutation operation

SMk-means:An Improved Mini Batch k-means Algorithm Based on Mapreduce with Big Data

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2018年第9期56卷 365-379页

作者： Bo Xiao Zhen Wang Qi Liu Xiaodong Liu Jiangsu key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computer and Software Nanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computing Edinburgh Napier University10 Colinton RoadEdinburghEH105DTUK

In recent years,the rapid development of big data technology has also been favored by more and more *** data storage and calculation problems have also been *** the same time,outlier detection problems in mass data have also come along with ***,more research work has been devoted to the problem of outlier detection in big ***,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect *** this paper,an improved algorithm is *** SMk-means is a fusion algorithm which is achieved by Mini Batch k-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of *** this paper,several experiments are performed to compare and analyze multiple performances of the *** analysis,we know that the proposed algorithm is superior to the existing algorithms.

关键词： Big data outlier detection SMk-means Mini Batch k-means simulated annealing

A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Mountain Science 2018年第7期15卷 1481-1497页

作者： kiyoumars ROUSHANGAR Farhad ALIZADEH Department of Water Resources Engineering Faculty of Civil EngineeringUniversity of Tabriz

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years(1960-2010) for 31 rain gauges(RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform(DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps(SOM) clustering techniques. Daubechies function(db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation(A) and detail(D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model's efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient(SC), Dunn index and Davis Bouldin index(DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3's homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.

关键词： Precipitation Discrete wavelet transform (DWT) k-means Self Organizing Map(SOM) Iran