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检索条件"主题词=K-means Algorithm"

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On Careful Selection of Initial Centers for k-means algorithm 3rd

On Careful Selection of Initial Centers for K-means Algorith...

引用

3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Jothi, R. Mohanty, Sraban kumar Ojha, Aparajita Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur Madhya Pradesh India

ISBN: (纸本)9788132225386;9788132225379

k-means clustering algorithm is rich in literature and its success stems from simplicity and computational efficiency. The key limitation of k-means is that its convergence depends on the initial partition. Improper selection of initial centroids may lead to poor results. This paper proposes a method known as Deterministic Initialization using Constrained Recursive Bi-partitioning (DICRB) for the careful selection of initial centers. First, a set of probable centers are identified using recursive binary partitioning. Then, the initial centers for k-means algorithm are determined by applying a graph clustering on the probable centers. Experimental results demonstrate the efficacy and deterministic nature of the proposed method.

关键词： Clustering k-means algorithm Initialization Bi-partitioning

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A New Fault Diagnosis Method Based on Component-wise Expectation-maximization algorithm and k-means algorithm 6

A New Fault Diagnosis Method Based on Component-wise Expecta...

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2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Bowen Hou Zhangming He Jiongqi Wang Bowen Sun kun Zhang College of Science National University of Defense Technology Beijing Institute of Control Engineering China Academy of Space Technology

ISBN: (纸本)9781509054626

In this paper, a fault diagnosis method is proposed based on component-wise expectation maximization algorithm and k-means algorithm, and it is applied to diagnosing the fault of the satellite attitude determination control system. First,Gaussian mixture model and the its traditional parameter estimation algorithm are reviewed. The component-wise expectationmaximization algorithm is used to estimate the parameters of Gaussian mixture model, which can lower the computational complexity of parameter estimation. Moreover, fault diagnosis, including detection and isolation, is carried out based on Gaussian mixture model, component-wise expectation maximization algorithm and k-means algorithm. Finally, the traditional method and our proposed method are applied for fault diagnosis on the satellite attitude determination control system. The simulation result shows that the new proposed method can, lower the computational complexity significantly, while the traditional and the new methods have nearly the same performance.

关键词： Fault Diagnosis Gaussian Mixture Model Component-Wise Expectation Maximization algorithm k-means algorithm Satellite Control System

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Research on fault diagnosis method of control moment gyroscope based on k-means algorithm 39

Research on fault diagnosis method of control moment gyrosco...

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39th Chinese Control Conference (CCC)

作者： Liu, Wenjing Yuan, Li Wang, Shuyi Liu, Chengrui Beijing Inst Control Engn Sci & Technol Space Intelligent Control Lab Beijing 100094 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

A fault diagnosis method of control moment gyroscope (CMG) based on k-means algorithm is proposed to improve the fault diagnosis accuracy by considering the hidden correlation between multidimensional telemetry data. Principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are used to extract features of CMG digital and physical data. On this basis, the fault diagnosis process of CMG based on k-means algorithm is supplied. Finally, the effectiveness of the proposed method is verified by using the actual in-orbit CMG data.

关键词： Control moment gyroscope Fault diagnosis PCA t-SNE k-means algorithm

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Improved k-means algorithm Based on Hybrid Fruit Fly Optimization and Differential Evolution 12

Improved K-Means Algorithm Based on Hybrid Fruit Fly Optimiz...

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12th International Conference on Computer Science and Education (ICCSE)

作者： Hu, Jixiong Wang, Chunzhi Liu, Chuan Ye, Zhiwei Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781509025084

Fruit fly optimization algorithm (FOA) is a new method for finding global optimization based on food finding behavior of the fruit fly. The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solution that are not zero. This paper proposes a hybrid fruit fly optimization and differential evolution (DEFOA) by modifying the expression of the smell concentration judgment value and by introducing a differential vector to replace the stochastic search. In this paper, we propose an improved k-means algorithm based on hybrid FOA and Differential Evolution (DE).

关键词： Fruit fly optimization Differential Evolution k-means algorithm Clustering algorithm

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Finding key Stations of Hangzhou Public Bicycle System by a Improved k-means algorithm

Finding Key Stations of Hangzhou Public Bicycle System by a ...

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International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2012)

作者： Xu, Haitao Wu, Hao Fang, XuJian Zhang, Wanjun Hangzhou Dianzi Univ Sch Software Engn Hangzhou 310012 Zhejiang Peoples R China

ISBN: (纸本)9783037854853

In China, Hangzhou is the first city to set up the Public Bicycle System. Now, the System has been the largest bike- sharing program in the world. The software of Hangzhou Public Bicycle System was developed by our team. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, determining how to find the key stations to give special care is very important. In this paper, an improved k-means algorithm is used to recognize the key stations of Hangzhou Public Bicycle System. At first, by passing over the two week's real data, a rent-return database is initialed. Then the algorithm builds minimum spanning tree and splits it to gets k initial cluster centers. The key stations are determined from the rent-return database by the algorithm. Practice examples and comparison with the traditional k-means algorithm are made. The results show that the proposed algorithm is efficient and robust. The research result has been applied in Hangzhou.

关键词： Public Bicycle System clustering k-means algorithm data mining

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PSO-based procedure to find number of clusters and better initial centroids for k-means algorithm: Image segmentation as case study 6

PSO-based procedure to find number of clusters and better in...

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6th International Conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Zarei, M. Alizadeh Nickfarjam, A. M. Islamic Azad Univ Kashan Dept Comp Engn Fac Elect & Comp Engn Kashan Iran Kashan Univ Med Sci Dept Hlth Informat Management & Technol Kashan Iran Kashan Univ Med Sci Res Ctr Hlth Informat Management Kashan Iran

ISBN: (纸本)9798350314557

In this paper, we propose a combination of k-means algorithm and Particle Swarm Optimization (PSO) method. The k-means algorithm is utilized for data clustering. On one hand, the number of clusters (k) should be determined by expert or found by try-and-error procedure in the k-means algorithm. On the other hand, initial centroids and number of clusters (k) are influenced on the quality of resulted grouping. Therefore, the aim of the proposed procedure is using PSO and the Structural Similarity Index (SSIM) criterion as a fitness function in order to find the best value for k parameter and better initial clusters' center. Due to different value of k parameter, the number of initial centroids which should be produced is variant. Thus, length of particles in PSO method may be different in each iteration. Experimental results show the superiority of this approach in comparison with standard k-means algorithm and both of them are evaluated on image segmentation problem.

关键词： Data Clustering Particle Swarm Optimization k-means algorithm Image Segmentation Structural Similarity Index

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A kernel Iterative k-means algorithm 40th

A Kernel Iterative K-Means Algorithm

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40th Anniversary International Conference on Information Systems Architecture and Technology (ISAT)

作者： Falkowski, Bernd-Juergen Fachhsch Okon & Management FOM Arnulfstr 30 D-80335 Munich Germany

ISBN: (纸本)9783030306045;9783030306038

In this paper Mercer kernels with certain invariance properties are briefly introduced and an apparently not well-known construction using certain cohomology groups is described. As a consequence some kernels arising from this are given. Hence a kernel version of an iterative k-means algorithm due to Duda et al. is exhibited. It resembles the usual k-means algorithm but relies on a different update procedure and allows an elegant computation of the target function.

关键词： k-means algorithm Mercer kernels Cohomology groups

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Automatic Parameter Tuning of k-means algorithm for Document Binarization 2018

Automatic Parameter Tuning of K-Means Algorithm for Document...

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7th ACM International Conference on Software Engineering and New Technologies (ICSENT)

作者： Gattal, Abdeljalil Abbas, Faycel Laouar, Mohamed Ridda Larbi Tebessi Univ Dept Math & Comp Sci Tebessa Algeria Bouira Univ Fac Sci & Appl Sci Comp Sci Dept LIMPAF Lab Bouira Algeria

ISBN: (纸本)9781450361019

The document binarization is a primary processing step toward document recognition system. It goals to separate the foreground from the document background. In this paper, we propose an algorithm for the binarization of document images degraded by using the clustering algorithm k-means with automatic parameter tuning. It uses the k-means algorithm to classify the document image into three classes as background, foreground and noise labels. Experimental results show that our method is more robust to the state of the art on recent benchmarks on the H-DIBCO 2016 dataset.

关键词： Document Binarization k-means algorithm Automatic parameter tuning H-DIBCO 2016 dataset

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Estimation of Clusters Number and Initial Centers of k-means algorithm Using Watershed Method 14

Estimation of Clusters Number and Initial Centers of K-means...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Wang, Xiaolong Jiao, Yiping Fei, Shumin Southeast Univ Sch Automat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467365932

In k-means clustering algorithm, the selection of cluster number k and initial k-means center has certain influence on the result. It would generate very different aggregation result when confronting with some certain types of data set. This paper aims at proposing an estimation method to evaluate the initial parameters for k-means algorithm. The estimation is executed through data analysis, which contains two main steps: the data would be transformed into data dimensional density first, and then, watershed method would be applied to divide the data space into multiple regions. Each regional center is selected as an initial k-means center, and the number of region is set as cluster number. This estimation method takes advantage of image segmentation ideology and the case study in this paper showed its favorable performance.

关键词： k-means algorithm watershed method clusters number initial k-means center

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Optimal location, sizing and allocation of sub-transmission substations using k-means algorithm

Optimal location, sizing and allocation of sub-transmission ...

引用

General Meeting of the IEEE-Power-and-Energy-Society

作者： Vahedi, Soroush Banejad, Mandi Assili, Mohsen Uinvcrs Shahrood Fac Elect & Robot Engn Shahrood Iran

ISBN: (纸本)9781467380409

The present article presents a novel and applied approach based on clustering to determine optimal locations, sizing of sub-transmission substations with their associated service area without determining location of candidate substations. The goal of this optimization is to minimize all of fixed costs and operation costs while all of constraints are met. Cost of equipment, construction, MV feeder cables, and power losses as well as existing substations are considered in cost function. Also we've considered different constraints such as voltage drop, substation power capacity limit, thermal limit, and radial network. Proposed model includes a method for applying planning constraints in k-means based algorithm that it leads to convert k-means based algorithm into an applied tool with most accuracy. We employed impact factor to adjust effect magnitude of load power to determine new sub-transmission locations. This algorithm is tested by real urban network to verify effectiveness and feasibility of proposed model.

关键词： Electrical power distribution substation expansion planning Sub-transmission substations Optimal location Sizing allocation of substations Clustering k-means algorithm

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