检索结果-内蒙古大学图书馆

34th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Qiao, Yuchen Li, Yunlu Lv, Xiaotian Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781728139364

In order to solve the problem of low efficiency of k-means algorithm in processing the data mining prediction problem of big data with more attributes, an annual income prediction method of residents based on improved k-means algorithm is proposed. The improved k-means algorithm combines the principal component analysis method with the traditional k-means algorithm. After reducing the dimensionality of various data attributes, the data are classified with k-means algorithm. The research makes use of 1994 U.S. census database and conducts a contrastive analysis of the two algorithms. The results show that the prediction accuracy has been significantly improved by 13.3313%, from 53.1016% to 66.4329% It is clear the improved algorithm can effectively improve the accuracy of clustering and annual income prediction.

关键词： k-means algorithm Various attributes Data mining Principal component analysis Annual income forecast

来源：评论

学校读者我要写书评

暂无评论

Agricultural Robot Navigation Path Recognition Based on k-means algorithm for Large-Scale Image Segmentation 14

Agricultural Robot Navigation Path Recognition Based on K-me...

引用

14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Mao, Jiandong Cao, Zhen Wang, Hongyan Zhang, Bai Guo, Zhen Niu, Wenqi North Minzu Univ Sch Elect & Informat Engn Yinchun Peoples R China Western Elect Commerce Co Ltd Yinchun Peoples R China

ISBN: (纸本)9781538694909

In order to solve the problem of agricultural robot navigation path recognition in the uneven illumination and complex background environment which lead to the poor accuracy of navigation path, a clustering algorithm for image segmentation is used in this paper. By introducing the Lab color space and k-means algorithm, the k-means clustering process can be performed with large-scale segmentation of the region of interest in the image. After clustered twice, the image can separate the path information of the farmland from background. The navigation path can be fitted by using the linear least squares method. For illustration, an image of the medlar farmland line is utilized to show the feasibility of this method. Experience results show that the method of clustering and segmenting the region of interest based on k-means algorithm can effectively improve the accuracy of image segmentation and solve the influence of uneven illumination and complex background environment on farmland navigation path accuracy.

关键词： Agricultural robot Lab color space k-means algorithm

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Path Planning Model Based on k-means algorithm and Simulated Annealing algorithm 3

A Dynamic Path Planning Model Based on K-means Algorithm and...

引用

IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Lu, Tongliang Fu, Jiangtao Hu, Xichun Natl Univ Def Technol Coll Informat & Commun Wuhan Hubei Peoples R China Natl Univ Def Technol Coll Elect Engn Hefei Anhui Peoples R China

ISBN: (纸本)9781538662434

Aiming at the problem that it is difficult to reduce logistics distribution cost through quantitative analysis, the paper takes the lowest logistics and transportation cost as objective function, comprehensively considers the location of logistics distribution centers, and constructs dynamic path planning model based on k-means algorithm and simulated annealing algorithm. Through simulation analysis, it can be found that by wisely selecting logistics distribution centers as transit service stations, the logistics distribution cost can be effectively reduced.

关键词： Path planning Simulated annealing k-means algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on the Construction of Digital Economy Index System Based on k-means-SA algorithm

引用

SAGE OPEN 2023年第4期13卷

作者： Su, Jinqi Dong, Changhong Su, ke He, Lin Xian Univ Posts & Telecommun Xian Shaanxi Peoples R China Tarim Univ Alar Xinjiang Peoples R China Tarim Univ Sch Econ & Management 1487 East Tarim Ave Alar 843300 Xinjiang Peoples R China

The digital economy is developing rapidly worldwide, which is of great strategic significance in leading and driving the process of high-quality and high-efficiency development of the economy. It has gradually become a new engine to promote China's economic growth. Therefore, it is vital to establish a digital economy indicator system. In this context, according to the connotation of digital economy, comprehensively consider the four dimensions of digital foundation, digital application, digital innovation and digital benefit, and build a full-scale digital economy indicator system. However, some previous literature proposed operating the k-means clustering algorithm in optimizing the index system. The algorithm, susceptible to the initial selection of cluster center, generates completely different clustering results with other random seed points and is thus unsuitable for optimizing the index system. Based on this, the advantages of GRA, SA, and k-means algorithms are combined to propose a k-means-SA algorithm that can obtain the global optimal solution. Then, by combining the k-means SA algorithm and the rough set algorithm, the constructed index system is further optimized. Ultimately, it establishes a set of all-round, multi-system and multi-dimensional digital economy index systems, which is of great reference significance for formulating relevant policies, provides scientific index support for the subsequent extension of digital economy theory, and promotes the benign growth of the worldwide economy. The importance of constructing and optimizing the digital economy indicator systemIn today's rapid development of the digital economy, a set of scientific, feasible, and practical application values of the digital economy evaluation index system is constructed to evaluate better and promote the development of the digital economy. Based on the four dimensions of digital foundation, digital application, digital innovation, and digital benefit, this paper constructs

关键词： digital economy index construction k-means algorithm k-means-SA algorithm

来源：评论

学校读者我要写书评

暂无评论

An Improved k-means algorithm Based on Intersection over Union for Network Security 11

An Improved K-means Algorithm Based on Intersection over Uni...

引用

IEEE 11th International Conference on Communication Software and Networks (ICCSN)

作者： Xu, Hui Fu, Chaochuan Yao, Shunyu Zong, Xinlu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728121840

As the network architecture becomes more and more complex, network intrusion behavior tends to diversify. In view of the characteristics that the era of big data is coming, it is imperative to find tools that can better protect current network security. In order to solve this problem, this paper proposes an improved k-means algorithm in the field of intrusion detection for network security, which is based on Intersection over Union in order to optimize initial clustering centers, with the consideration that the more different the data are, the more suitable the data act as the initial cluster centers. Experimental results show that, the improved k-means algorithm is superior to the original k-means algorithm in terms of overall precision, recall rate and F1_score under the same number of iterations through the data set of kDD CUP99.

关键词： network security intrusion detection k-means algorithm initial clustering center intersection over union

来源：评论

学校读者我要写书评

暂无评论

Improvement of k-means algorithm based on density 8

Improvement of K-means algorithm based on density

引用

IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zhang, Lanlan Qu, Jinshuai Gao, Minghu Zhao, Meina YunnanMinZu Univ Univ Key Lab Informat & Commun Secur Disaster Bac Kunming 6505031 Yunnan Peoples R China Yunnan MinZu Univ Univ Key Lab Wireless Sensor Networks Yunnan Prov Kunming Yunnan Peoples R China

ISBN: (纸本)9781538681787

In view of the shortcomings of the traditional k-means method, the D-k-means algorithm is proposed in this paper. The algorithm adopts the concept of density number. The point set of high density number is extracted from the original data set as a new training set, and the point in the point set of the high density number is selected as the initial cluster center point. Then, using the method of geometric center points to update the cluster center points at high density points until convergence conditions are reached. Experiments show that the method can effectively avoid the local optimal situation of k-means clustering algorithm. On the other hand, the iterative number of iteration in the clustering process is reduced, and the stability and accuracy of clustering are improved.

关键词： Clustering analysis k-means algorithm Dk-means algorithm high density number

来源：评论

学校读者我要写书评

暂无评论

Improved k-means algorithm on Home Industry Data Clustering in the Province of Bangka Belitung

Improved K-Means Algorithm on Home Industry Data Clustering ...

引用

Smart Technology and Applications (ICoSTA), 2020 International Conference on

作者： Hadi Santoso Hilyah Magdalena Department of Computer Science Information System Study Program STMIK Atma Luhur Pangkalpinang Bangka Indonesia

ISBN: (数字)9781728130835

ISBN: (纸本)9781728130842

The Government of Bangka Belitung Islands Province has not classified the home industry until now. Based on these problems, we propose a k-means algorithm for clustering home industry data. The k-means algorithm is widely used because it is straightforward and very suitable for grouping data. However, in its application, the k-means algorithm has a weakness in determining the starting point of the cluster center and, in its selection, is still carried out randomly. As a result, if the random value for initializing the initial centroid value is not right, then the grouping is less than optimal. Internal cluster validation is one way to determine the optimal cluster without knowing prior information from the data. This study aims to identify the optimal group by making improvements to the k-means algorithm and then to test it by applying an internal cluster, namely the Davies-Bouldin Index (DBI) and the Silhouette Index (SI) on the data of home industry in Bangka Belitung Island Province. The optimal cluster calculation results based on internal cluster validation both show that the Silhouette index and the DBI index with k = 3 on improved k-means algorithm. While the traditional k-means algorithm of internal cluster validation both show that the Silhouette index and the Davies-Bouldin Index with k = 2. The conclusion is k = 3 on the Davies-Bouldin Index of this research data gives good results for clustering home industry data in Bangka Belitung Islands Province.

关键词： k-means algorithm data clustering cluster validation index home industry home industry k-means algorithm data clustering Billiton

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

第三十九届中国控制会议

作者： Wenjing Liu Li Yuan Shuyi Wang Chengrui Liu Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering

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

来源：评论

学校读者我要写书评

暂无评论

Steady-state Security Assessment Based on k-means Clustering algorithm and Phasor Measurement Units

引用

RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2020年第4期13卷 559-570页

作者： Hemade, Bassam A. Ibrahim, Hamed A. Talaat, Hossam E. A. Suez Univ Fac Ind Educ Power & Elect Machines Dept Suez Egypt Future Univ Elect Power Engn Dept Fac Engn Cairo Egypt

Background: The security assessment plays a crucial role in the operation the modern interconnected power system network Methods: Hence, this paper addresses the application of k-means clustering algorithm equipped with Principal Component Analysis (PCA) and silhouette analysis for the classification of system security states. The proposed technique works on three principal axes;the first stage involves contingency quantification based on developed insecurity indices, the second stage includes dataset preparation to enhance the overall performance of the proposed method using PCA and silhouette analysis, and finally the application of the clustering algorithm over data. Results: The proposed composite insecurity index uses available synchronized measurements from Phasor Measurement Units (PMUs) to assess the development of cascading outages. Considering different operational scenarios and multiple levels of contingencies (up to N-3), Fast Decoupled Power Flow (FDPF) have been used for contingency replications. The developed technique applied to IEEE 14-bus and 57-bus standard test system for steady-state security evaluation. Conclusion: The obtained results ensure the robustness and effectiveness of the established procedure in the assessment of the system security irrespective of the network size or operating conditions.

关键词： Blackout contingency screening and ranking k-means algorithm unsupervised clustering techniques phasor measurement units principal components analysis silhouette analysis static security assessment synchronized measurements

来源：评论

学校读者我要写书评

暂无评论

VQ codebook design using modified k-means algorithm with feature classification and grouping based initialization

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2018年第7期77卷 8495-8510页

作者： Wang, Lang Lu, Zhe-Ming Ma, Long-Hua Feng, Ya-Pei Zhejiang Univ Ningbo Inst Technol Sch Informat Sci & Engn Ningbo 315100 Zhejiang Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Room 105Teaching Bldg 5Yuquan Campus Hangzhou 310027 Peoples R China

Vector quantization (VQ) has been successfully used in data compression and feature extraction areas. Codebook design is the essential step of VQ. The k-means algorithm is a famous data clustering technique which is also an efficient codebook design scheme. The main disadvantages of k-means algorithm lie in that the initial cluster centroids greatly affect the convergence speed and the final clustering performance. In the past two decades, many codebook initialization techniques have been proposed. However, most of these techniques do not make full use of the features of the training vectors, and some techniques require high extra computational load. This paper presents an efficient and simple technique for the conventional k-means algorithm based on feature classification and grouping. Firstly, all training vectors are classified into sixteen categories based on a two-level classifier including an edge classifier and a contrast classifier. Then, the training vectors in each category are sorted based on their norm values and divided into groups. Each group has the same size, and the centroid vector of each group is calculated as an initial codeword. Experimental results show that, compared with several typical initialization techniques, our technique can obtain a better codebook along with a faster convergence speed in a shorter time.

关键词： Vector quantization Codebook design Initial codebook generation k-means algorithm Image compression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：