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检索条件"主题词=K-means algorithm"

共 1243 条记录，以下是91-100 订阅

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Image Segmentation Method Based on Self Organizing Maps and k-means algorithm

Image Segmentation Method Based on Self Organizing Maps and ...

引用

9th Symposium on Neural Network Applications in Electrical Engineering

作者： Ristic, Dragan M. Pavlovic, Milan Reljin, Irini Univ Belgrade Fac Elect Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia ICT Coll YU-11000 Belgrade Serbia

ISBN: (纸本)9781424429035

In this paper, a method for color image segmentation based on kohonen's neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for regions centers. Secondly, using maxmin algorithm, number of candidates is reduced to initializing number of centers, which are then used for further analysis. During this process, initial number of regions is formed. For every formed region spatial and intensity centers are determined. Finally, in the third step, iterative procedure of modified k-means algorithm is realized during which the number of regions is minimized. The experimental results verify the usability of described algorithm.

关键词： Color image segmentation kohonen neural network self-organizing map k-means algorithm

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A Modified k-means algorithm - Two-Layer k-means algorithm 10

A Modified <i>K</i>-means Algorithm - Two-Layer <i>K</i>-mea...

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10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Liu, Chen-Chung Chu, Shao-Wei Chan, Yung-kuan Yu, Shyr-Shen Natl Chin Yi Univ Technol Taichung Dept Elect Engn Taichung Taiwan Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Dept Management Informat Syst Taichung 40227 Taiwan

ISBN: (纸本)9781479953905

In this paper, a modified k-means algorithm is proposed to categorize a set of data. k-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another. However, it is vulnerable to outliers and noisy data, and it spends much executive time in classifying data too. Noisy data, outliers, and the data with quite different values in one cluster may reduce the accuracy rate of data matching obtained by a pattern matching system since the cluster center cannot precisely describe the data in the cluster. Hence, this study provides a two-layer k-means algorithm to solve above problems. In experiment, several well-known data sets are used to evaluate the performance of proposed algorithm, and the two-layer k-means algorithm can give expressive experimental results.

关键词： k-means algorithm Classification subcluster

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Comparison of Hybrid ACO-k-means algorithm and Grub cut for MRI images segmentation 14

Comparison of Hybrid ACO-k-means algorithm and Grub cut for ...

引用

14th International Symposium on Intelligent Systems

作者： El-khatib, S. A. Skobtsov, Y. A. Rodzin, S., I Southern Fed Univ 105-42 Bolshaya Sadovaya str Rostov Na Donu 344006 Russia St Petersburg State Univ Aerosp Instrumentat 67 Bolshaya Morskaya Str St Petersburg 190000 Russia

Image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： MRI image segmentation ant colony optimization k-means algorithm swarm intelligence grub cut

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A Novel ELM k-means algorithm for Clustering 1

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5th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Alshamiri, Abobakr khalil Surampudi, Bapi Raju Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (数字)9783319202945

ISBN: (纸本)9783319202945;9783319202938

Extreme learning machine (ELM) as a new technology has shown its good generalization performance in regression and classification applications. Clustering analysis is an important tool to explore the structure of data and has been employed in many disciplines and applications. In this work, we propose a method that efficiently performs clustering in a high-dimensional space. The method builds on ELM projection into a high-dimensional feature space and the k-means algorithm for unsupervised clustering. The proposed ELM k-means algorithm is tested on twelve benchmark data sets. The experimental results indicate that ELM k-means algorithm can efficiently be used for multivariate data clustering.

关键词： Clustering Extreme learning machine k-means algorithm

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A Data Classification Method Using Genetic algorithm and k-means algorithm with Optimizing Initial Cluster Center

A Data Classification Method Using Genetic Algorithm and K-M...

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IEEE International Conference on Computer and Communication Engineering Technology (CCET)

作者： Shi, Haobin Xu, Meng Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781538674376

Aiming at the problems of the classical data classification method, this paper proposes a method using genetic algorithm and k-means algorithm to classify data. In order to improve the effectiveness of data analysis, considering that the classical k-means algorithm is easy to be influenced by the initial cluster center with random selection, this paper improves the k-means algorithm by using the method of optimizing the initial cluster center. This paper first uses the sorted neighborhood method (SNM) to preprocess the data, and then the k-means algorithm is used to cluster data. In order to improve the accuracy of the k-means algorithm, this paper optimizes the initial cluster center, and unifies the genetic algorithm for the data dimensionality reduction. The experimental results show that the proposed method has higher classification accuracy than the classical data classification method has.

关键词： data classification k-means algorithm sorted neighborhood method genetic algorithm

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FORMATION OF PART FAMILY IN RECONFIGURABLE MANUFACTURING SYSTEM USING PRINCIPLE COMPONENT ANALYSIS AND k-means algorithm

FORMATION OF PART FAMILY IN RECONFIGURABLE MANUFACTURING SYS...

引用

23rd International DAAAM Symposium on Intelligent Manufacturing and Automation - Focus on Sustainability

作者： Gupta, Ashutosh Jain, P. k. kumar, Dinesh

ISBN: (纸本)9783901509919

The Reconfigurable Manufacturing Systems (RMS) is the next step in manufacturing, allowing the production of any quantity of highly customised and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of Reconfigurable Manufacturing Systems (RMSs). The methodology carried out in three phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make parts groups while maximizing correlation between parts. In the third phase, Agglomerative Hierarchical k-means algorithm improved the parts family formation using Euclidean distance resulting in an optimum set of part families for reconfigurable manufacturing system.

关键词： Reconfigurable manufacturing system Part family formation Principle Component Analysis k-means algorithm Similarity coefficient Multivariate analysis

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Improved k-means algorithm Based on Hybrid Rice Optimization algorithm 9

Improved K-Means Algorithm Based on Hybrid Rice Optimization...

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9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

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

ISBN: (纸本)9781538606971

Clustering analysis is an active research branch in the area of data mining due to its simplicity and rapidity. However, k-means algorithm has the shortcomings of heavily depending on the initial clustering center and easily falls into local optimum. In this paper, we consider a deep research on k-means algorithm of optimization. We put forward the first selected initial clustering center of k-means algorithm, toward this end, a novel hybrid algorithm based on k-means algorithm and Hybrid Rice Optimization algorithm were proposed to rapidly find the optimal cluster centers and avoid getting into local optimum. Experimental results show that the proposed clustering algorithm outperforms other similar algorithms.

关键词： Clustering analysis k-means algorithm Hybrid Rice Optimization algorithm

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Voltage transformer secondary circuit anomaly detection based on k-means algorithm 3

Voltage transformer secondary circuit anomaly detection base...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Sun, Jianqiao Chen, Xiao Zhang, Wei Ding, Xunan Du, Junchi State Grid Zhejiang Mkt Ctr Hangzhou Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350375145;9798350375138

In the secondary circuit of voltage transformer in substation, there will be poor contact phenomenon, which will affect the accuracy of measurement and threaten the safe operation of power system. At present, regular inspection is used to check the power metering equipment, but the fault cannot be discovered immediately. In order to realize real-time detection of the voltage transformer's secondary circuit poor contact fault, this paper analyzes the data characteristics of the secondary circuit poor contact fault from the data, and establishes the secondary circuit abnormal detection model based on k-means algorithm to complete the voltage transformer's secondary circuit poor contact fault detection. The experimental results show that the secondary circuit anomaly detection method based on k-means algorithm proposed in this paper can effectively detect the number of anomalies, and the detection accuracy rate is more than 90%, which has a good detection effect and quality.

关键词： voltage transformer secondary circuit poor contact k-means algorithm anomaly detection

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An Analysis on the Weibo Topic Detection Based on k-means algorithm

An Analysis on the Weibo Topic Detection Based on K-means Al...

引用

IEEE International Conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Li, Meihua Wu, keqing Chen, Le Jiangxi Univ Sci & Technol Sch Sci Ganzhou Peoples R China Jiangxi Univ Sci & Technol Educ Informat Technol Ctr Ganzhou Peoples R China

ISBN: (纸本)9781665416061

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. k-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional k-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified k-means algorithm is proposed, which uses Jaccard distance measure for assigningthe most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. Theexperimental results demonstrate that the proposed k-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data.

关键词： k-means algorithm Word2vec Weibo topic detection

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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...

引用

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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