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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Salomo, Julian Siregar, Bakti Matana University Kab. Tangerang Indonesia

ISBN: (纸本)9789819738588

This article focuses on clustering 514 cities/districts in Indonesia based on their poverty levels from the pre-COVID-19 period (2019) until the near-end of the COVID-19 period (2022). Using publicly available data from the Central Statistics Agency (BPS) for 2019–2022, encompassing 11 poverty-related variables, the study employs unsupervised learning techniques. The process involves testing for multicollinearity;normalizing the data and applying Principal Component Analysis to reduce the variables to 2 principal components;determining the optimal number of clusters (3) is done using the elbow method, silhouette index, and gap statistic algorithm;the k-means algorithm is then applied to cluster the cities/districts each year. The analysis reveals three clusters: cluster 1 represents areas with low poverty levels, cluster 2 comprises regions with moderate levels, and cluster 3 includes areas with high poverty rates. The number of cities/districts in cluster 3 increased from 36 in 2019 to 40 in 2022, with 32 consistently belonging to this cluster. These findings highlight the importance of targeted government assistance to these areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cluster optimization k-means algorithm PCA Poverty data Unsupervised learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第26期81卷 37007-37032页

作者： Gupta, Manoj kumar Chandra, Pravin Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Sect 16C Delhi 110078 India Trinity Inst Innovat Profess Studies Greater Noida 201310 Uttar Pradesh India

Recently, Internet of Things (IoT) and multimedia are gaining popularity because of their usages in various applications. Numerous sensors and automated devices are generating huge volumes of data. Therefore, it is required to efficiently and effectively analyze this voluminous data. It can be achieved by using appropriate machine leaning techniques such as clustering. Among the clustering techniques, the k-means method/algorithm is one of the simplest, effective and commonly used methods. For making the cluster, it uses a measure of similarity/distance among the data observations. Nearby/similar data observations are placed within the same cluster whereas distant/dis-similar data observations are placed in other clusters. Hence, the similarity/distance metric plays a major role on the performance and accuracy of the k-means. Therefore, using an appropriate similarity/distance metric, the performance and accuracy of the k-means can be improved. k-means algorithm is majorly implemented using Euclidean distance metric. With the objective to explore the better and/or alternate similarity/distance metric(s) for k-means, a case study, based on empirical evaluation, of thirteen different similarity/distance metrics on six well-known datasets is performed and presented in this paper. By using the efficient and effective similarity / distance metrics, the performance and accuracy of the k-means algorithm can be improved which leads to formation of good clusters of various data observations or things or images etc. The results of the empirical study are analyzed and compared on the basis of widely used statistical clustering evaluation/validation measures. Based on the comparative results, these metrics are assigned with the ranks. Overall, the results demonstrate that Manhattan and Minkowski distance metrics gives better results for k-means algorithm.

关键词： Internet of things Data analytics and multimedia Similarity metrics Distance metrics k-means algorithm Clustering Machine learning

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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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Research on image text recognition based on canny edge detection algorithm and k-means algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第SUPPL 1期13卷 72-80页

作者： Wu, Fangsheng Zhu, Changan Xu, Jinxiu Bhatt, Mohammed Wasim Sharma, Ashutosh Anhui Business Vocat Coll Inst Informat Engn Hefei 231131 Anhui Peoples R China Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Cent Univ Punjab Bathinda India Southern Fed Univ Inst Comp Technol & Informat Secur Southern Fed Dist Russia

The latest research in the field of recognition of image characters has led to various developments in the modern technological works for the improvement of recognition rate and precision. This technology is significant in the field of character recognition, business card recognition, document recognition, vehicle license plate recognition etc. for smart city planning, thus its effectiveness should be improved. In order to improve the accuracy of image text recognition effectively, this article uses canny algorithm to process edge detection of text, and k-means algorithm for cluster pixel recognition. This unique combination combined with maximally stable extremal region and optimization of stroke width for image text yields better results in terms of recognition rate, recall, precision, F-score and accuracy. The results show that the correct recognition rate is 88.3% and 72.4% respectively with an accuracy value of 90.5% for the proposed method. This algorithm has high image text recognition rate, can recognize images taken in complex environment, and has good noise removal function. It is significantly an optimal algorithm for image text recognition.

关键词： Canny edge detection algorithm k-means algorithm Image text recognition Noise removal

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Text Clustering Based on Binary Particle Swarm and Spherical k-means algorithm 3

Text Clustering Based on Binary Particle Swarm and Spherical...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Luo, Luoyang Qiu, Linrun Guangdong University of Science and Technology College of Computer Science Dongguan523083 China

ISBN: (纸本)9798350341546

Internet technology advancement has led to an exponential surge in text data. Among the pressing data types today, natural language data stands out. Leveraging natural language processing (NLP) technology, computers can comprehend human language and extract valuable information from extensive textual data. This paper introduces a novel approach for text clustering, combining binary particle swarm optimization with the spherical k-means algorithm. Empirical findings on a Chinese text dataset demonstrate the effectiveness of the feature selection method introduced in this paper, effectively diminishing feature overlap and enhancing clustering precision. © 2023 IEEE.

关键词： Binary particle swarm algorithm k-means algorithm Text clustering

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Research on glass composition analysis based on k-means algorithm and Lasso regression algorithm 3

Research on glass composition analysis based on K-means algo...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Chen, Yutong Zhao, Chenrui Chen, Qiyan Li, Yuwan Fu, Tiantian Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350341546

This paper aims to study the relationship among weathering, color, species and ornamentation of glass relics by means of data preprocessing and statistical analysis. At the same time, the logistic regression model was used to classify the high potassium and lead barium glass. The k-means clustering algorithm is used to cluster the glass, and the chemical composition content rules of each class are found, and each class is regarded as a sub-class. Through the established k-means clustering algorithm to determine the central components of high-potassium and lead-barium glass, it is found that the central components of high-potassium glass are k2O and CaO, while the central components of lead-barium glass are SiO2, PbO and BaO. Through LASSO regression model based on Lars algorithm, 14 chemical elements were screened, and the main related elements of the two types of glass were obtained. It is found that SiO2 is strongly correlated in these two types of glass, while PbO and BaO are strongly correlated in lead-barium glass, but the correlation with high-potassium glass is very weak. This study is of great significance to elucidate the characteristics and classification of glass relics. © 2023 IEEE.

关键词： correlation coefficient k-means algorithm Lasso regression model logistic regression model

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Fast Inspection Image Identification Method for Digital Electromechanical Equipment Based on k-means algorithm

Fast Inspection Image Identification Method for Digital Elec...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Fu, Jiang Guizhou City Vocational College Guiyang Guizhou China

ISBN: (纸本)9798350313413

The research of image recognition method based on k-means algorithm for digital motor equipment rapid inspection has important background and research significance. It represents a major advance in the field of device inspection. The core idea of this method is to detect and recognize equipment state automatically through image clustering and model training, so as to reduce the dependence on manual inspection and reduce the error rate. Firstly, k-means algorithm is used to cluster the images to be detected and divide them into different categories. Then, model training for each category is carried out to realize the classification and recognition of each category. In this way, it can automatically analyze and identify the inspection images of the equipment, quickly and accurately judge the status of the equipment, and reduce the interference of human factors. Experimental results show that this method is efficient and accurate. The recognition time of each image is no more than 21 seconds, the accuracy is higher than 95%, the response time is less than 13 seconds, and the output time is less than 17 seconds. This is a significant improvement over traditional inspection methods. In summary, the image recognition method of digital motor equipment rapid inspection based on k-means algorithm has important application prospect and research significance in the field of equipment inspection. It improves inspection efficiency, accuracy and cost effectiveness, and is expected to promote further improvement and optimization of equipment inspection work.. © 2023 IEEE.

关键词： automatic detection digital design electromechanical equipment image identification k-means algorithm

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Methods for Generating Scene Animation Based on k-means algorithm 1

Methods for Generating Scene Animation Based on K-Means Algo...

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1st IEEE International Conference on Ambient Intelligence, knowledge Informatics and Industrial Electronics, AIkIIE 2023

作者： Zhang, Chulei Changchun Humanities and Sciences College Jilin Changchun130117 China

ISBN: (纸本)9798350316469

Animation scene design and production is an important part of the animation production process, which involves the layout and environmental design of the animation scene, and has a significant impact on the atmosphere, quality, and emotional expression of the animation. Based on the comparison of experimental results, it was found that compared with the evaluation of traditional and optimized scene animation generation methods by relevant staff, the average improvement in process optimization, output speed, work quality, and generation efficiency was 9%. Compared with traditional scene animation generation methods, the optimized scene animation generation method in this paper has higher scene image generation efficiency, can output more details of scene images, and greatly shorten the time required for animation production. This not only improves the quality of animation, but also promotes the integration of advanced information processing technology with the field of animation production. © 2023 IEEE.

关键词： Animation Production Generating Methods k-means algorithm Scene Image

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Research on mental health education of college students based on k-means algorithm 8

Research on mental health education of college students base...

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8th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Xu, Yixin Guo, Yanhong Chen, Jing Wuhan Business University Department of Marxism 430056 China Department of Basic Principles of Marxism China Wuhan Business University Department of Information Engineering 430056 China Data Science and Big Data Technology China

ISBN: (纸本)9798331517915

The research on the mental health education of college students is of great significance to improve the mental health level and quality of college students. In terms of research methods, there are both traditional statistical methods of survey data and current research methods of mathematical model algorithm analysis. This paper first collected the relevant data of 11943 college students, including age, gender, somatization, obsessive-compulsive disorder, interpersonal relationship and other 12 factors. Then descriptive analysis of the data is carried out on the basis of data collection. Finally, the k-means algorithm in cluster analysis is used to cluster the mental health problems of college students of all ages and the mental health problems of college students of all ages, and the clustering effect is better when k=4. After the final clustering results are obtained, the results are analyzed and relevant conclusions are drawn and relevant suggestions are put forward. The k-means algorithm used in this paper has a good effect on the analysis of mental health problems, and provides great help for improving the research of mental health education path of college students, which is of great significance. © 2025 IEEE.

关键词： Cluster analysis Descriptive analysis k-means algorithm mental health education

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Research on the Classification of E-Commerce Users Based on RFM Model and k-means algorithm

Research on the Classification of E-Commerce Users Based on ...

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

作者： Yao, Yingzhu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

The report provides an overview of the dataset and the models and algorithms applied in the project. Initially, we present a comprehensive review of the RFM model and clustering algorithms. Utilizing the online transaction data from a British e-retail company, we compile the data, apply the RFM model to extract user segmentation information, devise k-means algorithms, and ultimately leverage user behavior to efficiently meet user demands. This approach facilitates precise user targeting, minimizes marketing expenses, and maximizes overall returns. © 2023 IEEE.

关键词： clustering algorithm k-means algorithm RFM Model user classification

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