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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Shen, Wei Sun, Yueshi N China Elect Power Univ Coll Econ & Management Beijing Peoples R China

ISBN: (纸本)9783037850176

Electric load forecasting is an important aspect in the operation of energy market. Many researchers have tried various methods and have achieved considerable results. In this paper, we used Radial Basis Function Neural Network (RBFN) to train data and forecast daily maximum electric load of a costal city in North China. In order to have a better result, we introduced Artificial Fish Swarm algorithm (AFSA) to optimize RBF and adjust the center of k-means clustering algorithm. Data mining techniques were also employed to select indicators with impact on electric load. By comparing the forecast values and actual data, we arrived at conclusion that RBF optimized by AFSA could produce accurate result in forecasting daily maximum electric load. We also found that climate factors (temperature, humidity and air-pressure) had significant impact on daily maximum electric load.

关键词： AFSA RBFN k-means clustering algorithm Data Mining Daily Maximum Electric Load

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Detection of Ginseng Leaf Cicatrices Base on k-means clustering algorithm 10

Detection of Ginseng Leaf Cicatrices Base on K-means Cluster...

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10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Liu, Yuan-yuan Zhou, Shi-yu Sun, Jia-hui Jilin Agr Univ JLAU Coll Informat Technol Changchun Jilin Peoples R China Agr Sci & Technol Network Collaborat Innovat Ctr Changchun Jilin Peoples R China Internet Things Engn Res Technol Ctr Jilin Prov Changchun Jilin Peoples R China

ISBN: (纸本)9781538619377

Diseases like black spot, anthracnose and Cylindrocarpon destructans always appear on ginseng leaf. The traditional way to distinguish the disease in farmland is checking by planter in person. This way takes more time and energy. In this paper, according to cicatrices are different with normal leaf in color, we use k-means clustering algorithm and combine the difference between a and b components in Lab color space to discern the cicatrices in leaf image. Firstly, we can distinguish cicatrices and normal leaves. Secondly, we take out useless part like petiole. Lastly, we calculate the ratio of area of cicatrices and normal leaf. The ratio can be used as reference of the ginseng disease and its degree. The result of experiment shows that k-means clustering algorithm can segment the leaf image, and the segmentation is based on the difference of color, then we can calculate the ratio of pixel in each part, and judge the degree of disease.

关键词： digital image processing k-means clustering algorithm detection of ginseng's disease RGB color space Lab color space

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Study of Seepage Properties of Fractured Rock Mass Based on Improved k-means clustering algorithm

Study of Seepage Properties of Fractured Rock Mass Based on ...

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2nd International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2013)

作者： Guo, Haiqing Wen, Bo Bai, Xiaofeng Hohai Univ Minist Educ Geomech & Embankment Engn Key Lab Nanjing 210098 Jiangsu Peoples R China

ISBN: (纸本)9783037858578

Seepage properties of fractured rock mass are of prime importance for hydiaulic engineering and accurate description of rock fracture geometry parameters is an important and basic task in rock hydraulics. In this paper, an improved k-means clustering algorithm for structural plane of fractured rock mass was first brought forward and the corresponding Matlab program for discontinuity orientations partitioning was compiled and then used in the fitting analysis of dominant orientations of certain dam foundation rock mass. On this basis, combining calculation formulas of multi-group fractures, the permeability tensor and principle value was calculated for the actual dam foundation. The results provide a theoretical and computational reference for other similar projects.

关键词： k-means clustering algorithm discontinuities dominant orientation permeability tensor

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Application Of Improved k-means clustering algorithm In Transit Data Collection

Application Of Improved K-means Clustering Algorithm In Tran...

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2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010)

作者： Wu, Xueying Yao, Chunlong Dalian Polytech Univ Informat Sci & Engn Coll Dalian Peoples R China

ISBN: (纸本)9781424464968

Timely, accurate and complete transits data are the prerequisite of improving public transportation query system service level. It will generate a lot of redundant data by using the GPS terminal to collect transit site data, due to differences in the location of the same name site and the existing GPS system deviation. Therefore an improved k-means clustering algorithm was proposed, which was applied into clustering analysis of transit data with the same site name but different location. Experimental results show that the algorithm is effective and clustering results accord with the actual situation.

关键词： k-means clustering algorithm GPS Transit Site Public Transportation Query System

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Research on Load clustering Based on Singular Value Decomposition and k-means clustering algorithm

Research on Load Clustering Based on Singular Value Decompos...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Wang, Jiao Wang, kaiyan Jia, Rong Chen, Xiaofei Xian Univ Technol Sch Elect Engn Xian Peoples R China Shaanxi Reg Elect Power Co LTD Xianyang Elect Power Supply Xianyang Peoples R China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167824

Considering the shortcomings of existing clustering algorithms in clustering quality, this paper proposes a load clustering research based on singular value decomposition and k-means clustering algorithm. First, eight characteristic indexes of load are extracted, and the singular value is used to decompose the load characteristics of the user side. The solved singular value reflects the importance of this type of load characteristic. The load characteristics corresponding to the data with large singular value are taken as the main load characteristics to complete the dimensionality reduction of the data. Then, the clustering evaluation index SSE is used to compare the effects of direct clustering of load characteristics and clustering after singular value decomposition. The results show that the proposed method has better clustering effect on load characteristics. Finally, k-means clustering algorithm is used to cluster the load characteristics.

关键词： singular value decomposition k-means clustering algorithm load characteristic Dimensionality reduction of data cluster evaluation index

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Application of Big Data Technology and k-means clustering algorithm in Urban Rail Transit

Application of Big Data Technology and K-Means Clustering Al...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wu, Huijiao Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

With the continuous development of science and technology, the amount of data generated in life is increasing, and big data technology has now penetrated into all walks of life. This paper focuses on the application of big data technology in urban rail transit. Firstly, the related concepts of big data technology and urban rail transit are introduced in detail. Secondly, it emphasizes the necessity of using big data technology in urban rail transit;Finally, this paper expounds the examples of the application of big data technology in urban rail transit, including key applications such as operation and maintenance, accessibility analysis and passenger flow monitoring. Taking passenger flow monitoring as an example, based on the passenger flow data of Tianjin big data technology, the stations are classified by k-means clustering algorithm. Big data technology can help urban rail transit improve its operational efficiency in many aspects, promote its intelligent development and accelerate the process of urbanization. Faced with the abundance and complexity of data in the era of science and technology, it will become an inevitable trend to assist management decision-making through big data technology. © 2023 IEEE.

关键词： Accessibility model Big data technology k-means clustering algorithm Passenger flow monitoring Urban rail transit

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k-means clustering algorithm for Large-scale Chinese Commodity Information Web Based on Hadoop

K-means Clustering Algorithm for Large-scale Chinese Commodi...

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第十四届分布式计算及其应用国际学术研讨会

作者： Geng Yushui Zhang Lishuo School of Information Qilu University of Technology

With the growing popularity of the network, product information filled in the many pages of the Internet, which you want to get the information you need on these pages tend to consider clustering information, and the current explosive growth of data so that the information mass storage condition occurs, clustering to facing the problems such as large calculation complexity and time consuming, then the traditional k-means clustering algorithm does not meet the needs of large data environments today, so this article combined with the advantages of the Hadoop platform and MapReduce programming model is proposed the k-means clustering algorithm for large-scale chinese commodity information Web based on Hadoop. Map function calculates the distance from the cluster center for each sample and mark to their category, Reduce function intermediate results are summarized and calculated new clustering center for the next round of iteration. Experimental results show that this method can better improve the clustering processing speed.

关键词： k-means clustering algorithm Hadoop platform MapReduce Cloud computing Big Data

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Information Architecture (IA): Using Multidimensional Scaling (MDS) and k-means clustering algorithm for Analysis of Card Sorting Data

JOURNAL OF USABILITY STUDIES

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JOURNAL OF USABILITY STUDIES 2018年第3期13卷 138-157页

作者： Paea, Sione Baird, Ross Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji Optimal Workshop Www Optimalworkshop Com Wellington New Zealand

We present a method for visualizing and analyzing card sorting data aiming to develop an in-depth and effective information architecture and navigation structure. One of the well-known clustering techniques for analyzing large data sets is with the k-means algorithm. However, that algorithm has yet to be widely applied to analyzing card sorting data sets to measure the similarity between cards and result displays using multidimensional scaling. The multidimensional scaling, which employs particle dynamics to the error function minimization, is a good candidate to be a computational engine for interactive card sorting data. In this paper, we apply the combination of a similarity matrix, a k-means algorithm, and multidimensional scaling to cluster and calculate an information architecture from card sorting data sets. We chose card sorting to improve an information architecture. The proposed algorithm handled the overlaps between cards in the card sorting data quite well and displayed the results in a basic layout showing all clusters and card coordinates. For outliers, the algorithm allows grouping of single cards to their closest core clusters. The algorithm handled outliers well choosing cards with the strongest similarities from the similarity matrix. We tested the clustering algorithm on real-world data sets and compared to other techniques. The results generated clear knowledge on relevant usability issues in visualizing information architecture. The identified usability issues point to a need for a more in-depth search of design solutions that are tailored for the targeted group of people who are struggling with complicated visualizing techniques. This study is for people who need support to easily visualize information architecture from data sets.

关键词： information architecture card sorting multidimensional scaling k-means clustering algorithm analysis similarity matrix

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Comparison and Detection Analysis of Network Traffic Datasets Using k-means clustering algorithm

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JOURNAL OF INFORMATION & kNOWLEDGE MANAGEMENT 2020年第3期19卷

作者： Al-Sanjary, Omar Ismael Bin Roslan, Muhammad Aiman Helmi, Rabab Alayham Abbas Ahmed, Ahmed Abdullah Management & Sci Univ Fac Informat Sci & Engn Shah Alam 40100 Malaysia Qaiwan Int Univ QIU Fac Engn & Sci Sulaymaniyah Kurdistan Regio Iraq

Anomaly detection in specific datasets involves the detection of circumstances that are common in a homogeneous data. When looking at network traffic data, it is generally difficult to determine the type of attack without proper analysis and this holds true when simply viewing a record of network traffic with thousands of internet users to detect malicious activity. However, there are different types of datasets in light of the way they record or acquire data and facts. The paper aims to compare and analyse multiple datasets including NSL-kDD and MAWI by using k-means clustering algorithm. Specifically, the paper analyses the blind-Spots of the datasets and evaluates the most suitable dataset for k-means clustering algorithm. This paper's quantitative data analysis results are helpful in evaluating weaknesses of each dataset of traffic data, when using k-means clustering algorithm.

关键词： NSL-kDD MAWI k-means clustering algorithm

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Digital Visual Design Reengineering and Application Based on k-means clustering algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第4期11卷 1-13页

作者： Ren, Lijie kim, Hyunsuk Hongik Univ Seoul 100744 South Korea

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability. OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved k-means clustering algorithm. METHODS: A visual gene extraction method based on an improved k-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based k-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments. RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the k-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the k-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the k-means-JSO algorithm converges faster and with better accuracy compared to the k-means-ABC, k-means-GWO, k-means-DE, k-means-CMAES, and k-means-WWCD algorithms. CONCLUSION: The color extraction method of the k-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalizationability, and inefficiency in visual gene extraction methods.

关键词： digital visual design reengineering jellyfish optimization algorithm k-means clustering algorithm color gene extraction

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