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Relevant bibliographies by topics / Dynamic K-means algorithm

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Academic literature on the topic 'Dynamic K-means algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 13 July 2025

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Journal articles on the topic "Dynamic K-means algorithm"

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Andryan, Muhammad, Muhammad Faisal, and Ririen Kusumawati. "K-Means Binary Search Centroid With Dynamic Cluster for Java Island Health Clustering." Jurnal Riset Informatika 5, no. 3 (2023): 539–46. http://dx.doi.org/10.34288/jri.v5i3.511.

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This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with deficient health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies

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Saputra, Muhammad Andryan Wahyu, Muhammad Faisal, and Ririen Kusumawati. "K-Means Binary Search Centroid with Dynamic Cluster for Java Island Health Clustering." Jurnal Riset Informatika 5, no. 3 (2023): 269–76. http://dx.doi.org/10.34288/jri.v5i3.218.

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This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with poor health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies.

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Bangoria, Bhoomi Mansukhlal, Sweta S. Panchal, Sandipkumar R. Panchal, Janvi M. Maheta, and Sweety R. Dhabaliya. "Multidimensional Dynamic Destination Recommender Search System Employing Clustering: A Machine Learning Approach." Indian Journal Of Science And Technology 17, no. 40 (2024): 4187–97. http://dx.doi.org/10.17485/ijst/v17i40.2266.

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Objectives: Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and significance. According to the city first find latitude and longitude for precise clustering. Future work will incorporate optimization techniques to improve cluster formation recommendation accuracy. Findings: Clustering (unsupervised learning) is a separation technique that involves assigning locations to corresponding subsets of related clusters. The weighted K-means clustering algorithm is used with the elbow method which is used for discovering the optimum number of clusters. In weighted K-means algorithm for clustering uses scaling factor wi which transforms the impression of individual features to the whole distance calculation. It signifies the meaning of the ith feature in the perspective of the grouping task. Offering a scaling factor permits additional tractability in modifying the outcome of specific features on the distance calculation. It enables customization of the distance metric constructed on the specific requirements and characteristics of the records and clustering task. In this study, user can select multiple dimensions of their choice and get recommendations according to their choice. The proposed weighted K-means algorithm shows a significant improvement in accuracy which considers the proportion of correct recommendations out of all recommendations. A comparison with traditional K-means was conducted, where the weighted algorithm achieved a 17% higher accuracy due to its ability to give importance to specific features. The future version of the proposed system will incorporate optimization techniques for enhanced performance. Novelty: The suggested solution in this paper demonstrates that the user can enter the city of their choice. The recommended method indicates the city and nearby predilections once the user has selected their parameters, such as consuming formations or name or type. The ratio of relevant destinations that have been successfully recommended is 18% more compared to the K-means clustering algorithm. Keywords: Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted K-means clustering Algorithm

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Mononteliza, Jennefer. "Improved K-means Clustering Algorithm based on Dynamic Clustering." Asia-pacific Journal of Convergent Research Interchange 6, no. 4 (2020): 1–12. http://dx.doi.org/10.21742/apjcri.2020.04.01.

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Zheng, Liguo. "Improved K-Means Clustering Algorithm Based on Dynamic Clustering." International Journal of Advanced Research in Big Data Management System 4, no. 1 (2020): 17–26. http://dx.doi.org/10.21742/ijarbms.2020.4.1.02.

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Zhang, Tong Jie, Yan Cao, and Xiang Wei Mu. "Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm." Applied Mechanics and Materials 511-512 (February 2014): 904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.904.

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An algorithm of weighted k-means clustering is improved in this paper, which is based on improved genetic algorithm. The importance of different contributors in the process of manufacture is not the same when clustering, so the weight values of the parameters are considered. Retaining the best individuals and roulette are combined to decide which individuals are chose to crossover or mutation. Dynamic mutation operators are used here to decrease the speed of convergence. Two groups of data are used to make comparisons among the three algorithms, which suggest that the algorithm has overcome the problems of local optimum and low speed of convergence. The results show that it has a better clustering.

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Bangoria, Bhoomi Mansukhlal, Sweta S. Panchal, Sandipkumar R. Panchal, and Tusharkumar Mansukhbhai Bangoria. "A Novel Machine Learning Approach for Multidimensional Dynamic Destination Recommender S earch System Employing Clustering using Optimization Techniques." Indian Journal Of Science And Technology 17, no. 44 (2024): 4679–93. https://doi.org/10.17485/ijst/v17i44.3525.

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Objectives: To find the most suitable destinations according to a selection of various dimensions by the user. Optimization techniques are applied to clustering with the help of various objective functions, to find optimal clusters for better recommendations. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm and uses optimization techniques to improve cluster formation recommendation accuracy. This study used a data set from Kaggle, which considers different city names, types, and significance. According to city names, using a geolocator object in Python gets latitude and longitude for precise clustering. Findings: The elbow method utilizes the K-means and weighted K-means clustering algorithm to determine the number of clusters. We use optimization techniques such as the Artificial Bee Colony (ABC) algorithm and Particle Swarm Optimisation (PSO) to improve the cluster formation recommendation accuracy, as demonstrated by the following results: The accuracy of K-means with ABC and PSO is respectively 85% and 88%, while weighted K-means with ABC and PSO is respectively 90% and 93%. Novelty: This study highlights how advanced optimization techniques like ABC and PSO enhance K-means and weighted K-means clustering accuracy, precision, and recall. The combination of weighted K-means and PSO further enhances performance, making it the perfect choice for tasks that demand high-quality clustering and recommendation systems. Compared to the K-means clustering algorithm with PSO optimization, the ratio of successfully recommended relevant destinations is 7% higher. Keywords: Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted Kmeans clustering Algorithm, ABC, PSO

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Rosyid, Harunur, Muhammad Modi Bin Lakulu, and Ramlah Bt. Mailok. "Hybrid ABC–K Means for Optimal Cluster Number Determination in Unlabeled Data." Mobile and Forensics 6, no. 2 (2024): 52–65. http://dx.doi.org/10.12928/mf.v6i2.11529.

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This study presents the ABC K Means GenData algorithm, an enhancement over traditional K Means clustering that integrates the Artificial Bee Colony (ABC) optimization approach. The ABC K Means GenData algorithm addresses the issue of local optima commonly encountered in standard K Means algorithms, offering improved exploration and exploitation strategies. By utilizing the dynamic roles of employed, onlooker, and scout bees, this approach effectively navigates the clustering space for categorical data. Performance evaluations across several datasets demonstrate the algorithm's superiority. For the Zoo dataset, ABC K Means GenData achieved high Accuracy (0.8399), Precision (0.8089), and Recall (0.7286), with consistent performance compared to K Means and Fuzzy K Means. Similar results were observed for the Breast Cancer dataset, where it matched the Accuracy and Precision of K Means and surpassed Fuzzy K Means in Precision and Recall. In the Soybean dataset, the algorithm also performed excellently, showing top scores in Accuracy, Precision, Recall, and Rand Index (RI), outperforming both K Means and Fuzzy K Means.. The comprehensive results indicate that ABC K Means GenData excels in clustering categorical data, providing robust and reliable performance. Future research will explore its application to mixed data types and social media datasets, aiming to further optimize clustering techniques. .

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Bhoomi, Mansukhlal Bangoria, S. Panchal Sweta, R. Panchal Sandipkumar, M. Maheta Janvi, and R. Dhabaliya Sweety. "Multidimensional Dynamic Destination Recommender Search System Employing Clustering: A Machine Learning Approach." Indian Journal of Science and Technology 17, no. 40 (2024): 4187–97. https://doi.org/10.17485/IJST/v17i40.2266.

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Abstract <strong>Objectives:</strong> Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. <strong>Methods:</strong> This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and significance. According to the city first find latitude and longitude for precise clustering. Future work will incorporate optimization techniques to improve cluster formation recommendation accuracy. <strong>Findings:</strong> Clustering (unsupervised learning) is a separation technique that involves assigning locations to corresponding subsets of related clusters. The weighted K-means clustering algorithm is used with the elbow method which is used for discovering the optimum number of clusters. In weighted K-means algorithm for clustering uses scaling factor wi which transforms the impression of individual features to the whole distance calculation. It signifies the meaning of the ith feature in the perspective of the grouping task. Offering a scaling factor permits additional tractability in modifying the outcome of specific features on the distance calculation. It enables customization of the distance metric constructed on the specific requirements and characteristics of the records and clustering task. In this study, user can select multiple dimensions of their choice and get recommendations according to their choice. The proposed weighted K-means algorithm shows a significant improvement in accuracy which considers the proportion of correct recommendations out of all recommendations. A comparison with traditional K-means was conducted, where the weighted algorithm achieved a 17% higher accuracy due to its ability to give importance to specific features. The future version of the proposed system will incorporate optimization techniques for enhanced performance. <strong>Novelty:</strong> The suggested solution in this paper demonstrates that the user can enter the city of their choice. The recommended method indicates the city and nearby predilections once the user has selected their parameters, such as consuming formations or name or type. The ratio of relevant destinations that have been successfully recommended is 18% more compared to the K-means clustering algorithm. <strong>Keywords:</strong> Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted K-means clustering Algorithm

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Munirah, Aslan Alwi, Sudarno, and Andy Triyanto. "K-ALLY BASED DYNAMIC FUZZY CLUSTERING FOR GEOPOLITICAL ALLIANCE ANALYSIS: A CASE STUDY INSPIRED BY THE RUSSIAN-UKRAINIAN CONFLICT." Jurnal Teknik Informatika (Jutif) 6, no. 1 (2025): 419–26. https://doi.org/10.52436/1.jutif.2025.6.1.907.

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Geopolitical alliances are often based on a combination of factors such as geographic proximity, military strength, and strategic interests. In this research, we introduce the K-Ally algorithm based on Dynamic Fuzzy Clustering to dynamically analyze alliance patterns between countries. Using fuzzy logic and adaptive thresholds, this algorithm evaluates the potential benefits of alliances based on key attributes, such as geographic distance and power differences. This study is inspired by the allied dynamics that emerged in the Russian-Ukrainian war, where changes in strategy and international relations were key to the continuation of the conflict. The paper also compare this algorithm with the K-Means method commonly used in geopolitical data analysis. Experimental results show that K-Ally based on Dynamic Fuzzy Clustering is able to capture alliance dynamics better than K-Means, especially in conditions of uncertainty or attribute imbalance between countries. This research contributes to the development of new analytical tools for the study of geopolitics and international conflict.

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Dissertations / Theses on the topic "Dynamic K-means algorithm"

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Xie, Qing Yan. "K-Centers Dynamic Clustering Algorithms and Applications." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644.

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Madjar, Nicole, and Filip Lindblom. "Machine Learning implementation for Stress-Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280897.

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This project is about trying to apply machine learning theories on a selection of data points in order to see if an improvement of current methodology within stress detection and measure selecting could be applicable for the company Linkura AB. Linkura AB is a medical technology company based in Linköping and handles among other things stress measuring for different companies employees, as well as health coaching for selecting measures. In this report we experiment with different methods and algorithms under the collective name of Unsupervised Learning, to identify visible patterns and behaviour of data points and further on we analyze it with the quantity of data received. The methods that have been practiced on during the project are “K-means algorithm” and a dynamic hierarchical clustering algorithm. The correlation between the different data points parameters is analyzed to optimize the resource consumption, also experiments with different number of parameters are tested and discussed with an expert in stress coaching. The results stated that both algorithms can create clusters for the risk groups, however, the dynamic clustering method clearly demonstrate the optimal number of clusters that should be used. Having consulted with mentors and health coaches regarding the analysis of the produced clusters, a conclusion that the dynamic hierarchical cluster algorithm gives more accurate clusters to represent risk groups were done. The conclusion of this project is that the machine learning algorithms that have been used, can categorize data points with stress behavioral correlations, which is usable in measure testimonials. Further research should be done with a greater set of data for a more optimal result, where this project can form the basis for the implementations.<br>Detta projekt handlar om att försöka applicera maskininlärningsmodeller på ett urval av datapunkter för att ta reda på huruvida en förbättring av nuvarande praxis inom stressdetektering och åtgärdshantering kan vara applicerbart för företaget Linkura AB. Linkura AB är ett medicintekniskt företag baserat i Linköping och hanterar bland annat stressmätning hos andra företags anställda, samt hälso-coachning för att ta fram åtgärdspunkter för förbättring. I denna rapport experimenterar vi med olika metoder under samlingsnamnet oövervakad maskininlärning för att identifiera synbara mönster och beteenden inom datapunkter, och vidare analyseras detta i förhållande till den mängden data vi fått tillgodosett. De modeller som har använts under projektets gång har varit “K-Means algoritm” samt en dynamisk hierarkisk klustermodell. Korrelationen mellan olika datapunktsparametrar analyseras för att optimera resurshantering, samt experimentering med olika antal parametrar inkluderade i datan testas och diskuteras med expertis inom hälso-coachning. Resultaten påvisade att båda algoritmerna kan generera kluster för riskgrupper, men där den dynamiska modellen tydligt påvisar antalet kluster som ska användas för optimalt resultat. Efter konsultering med mentorer samt expertis inom hälso-coachning så drogs en slutsats om att den dynamiska modellen levererar tydligare riskkluster för att representera riskgrupper för stress. Slutsatsen för projektet blev att maskininlärningsmodeller kan kategorisera datapunkter med stressrelaterade korrelationer, vilket är användbart för åtgärdsbestämmelser. Framtida arbeten bör göras med ett större mängd data för mer optimerade resultat, där detta projekt kan ses som en grund för dessa implementeringar.

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Jurásek, Petr. "Shlukování proteinových sekvencí na základě podobnosti primární struktury." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236761.

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This master's thesis consider clustering of protein sequences based on primary structure of proteins. Studies the protein sequences from they primary structure. Describes methods for similarities in the amino acid sequences of proteins, cluster analysis and clustering algorithms. This thesis presents concept of distance function based on similarity of protein sequences and implements clustering algorithms ANGES, k-means, k-medoids in Python programming language.

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Book chapters on the topic "Dynamic K-means algorithm"

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Kumar, Narender, Vinesh Kumar Jain, and Jyoti Gajrani. "K-Means Algorithm to Form Dynamic Cluster Formation to Counter the Static Property of K-Means." In Proceedings of International Conference on Data Science and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6634-7_32.

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Henzinger, Monika, David Saulpic, and Leonhard Sidl. "Experimental Evaluation of Fully Dynamic k-Means via Coresets." In 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX). Society for Industrial and Applied Mathematics, 2024. http://dx.doi.org/10.1137/1.9781611977929.17.

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Ding, Jianli, Wansheng Tang, and Liuqing Wang. "Parallel Combination of Genetic Algorithm and Ant Algorithm Based on Dynamic K-Means Cluster." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_103.

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Lapegna, Marco, Valeria Mele, and Diego Romano. "An Adaptive Strategy for Dynamic Data Clustering with the K-Means Algorithm." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43222-5_9.

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Rathod, Dushyantsinh, Samrat Khanna, and Manish Singh. "Smart Two Level K-Means Algorithm to Generate Dynamic User Pattern Cluster." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_19.

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Zriaa, Rajae, and Said Amali. "Recommendation of Learning Objects Through Dynamic Learning Style Identification and k-means Clustering Algorithm." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90633-7_62.

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Laccetti, Giuliano, Marco Lapegna, Valeria Mele, and Diego Romano. "A High Performance Modified K-Means Algorithm for Dynamic Data Clustering in Multi-core CPUs Based Environments." In Internet and Distributed Computing Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_9.

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Borg Anton, Lavesson Niklas, and Boeva Veselka. "Comparison of Clustering Approaches for Gene Expression Data." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-330-8-55.

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Clustering algorithms have been used to divide genes into groups according to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently indicates that the genes could possibly share a common biological role. In this paper, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expression data using Dynamic TimeWarping distance in order to measure similarity between gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for estimating the quality of clusters, Jaccard Index for evaluating the stability of a cluster method and Rand Index for assessing the accuracy. The obtained results are analyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices.

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Hichem, Bennasr, and M’Sahli Faouzi. "An Optimization Procedure of Model’s Base Construction in Multimodel Representation of Complex Nonlinear Systems." In Optimization Problems in Engineering [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96458.

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The multimodel approach is a research subject developed for modeling, analysis and control of complex systems. This approach supposes the definition of a set of simple models forming a model’s library. The number of models and the contribution of their validities is the main issues to consider in the multimodel approach. In this chapter, a new theoretical technique has been developed for this purpose based on a combination of probabilistic approaches with different objective function. First, the number of model is constructed using neural network and fuzzy logic. Indeed, the number of models is determined using frequency-sensitive competitive learning algorithm (FSCL) and the operating clusters are identified using Fuzzy K- means algorithm. Second, the Models’ base number is reduced. Focusing on the use of both two type of validity calculation for each model and a stochastic SVD technique is used to evaluate their contribution and permits the reduction of the Models’ base number. The combination of FSCL algorithms, K-means and the SVD technique for the proposed concept is considered as a deterministic approach discussed in this chapter has the potential to be applied to complex nonlinear systems with dynamic rapid. The recommended approach is implemented, reviewed and compared to academic benchmark and semi-batch reactor, the results in Models’ base reduction is very important witch gives a good performance in modeling.

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Tripathi, Ashish Kumar, Kapil Sharma, and Manju Bala. "Parallel Hybrid BBO Search Method for Twitter Sentiment Analysis of Large Scale Datasets Using MapReduce." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch063.

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Sentiment analysis is an eminent part of data mining for the investigation of user perception. Twitter is one of the popular social platforms for expressing thoughts in the form of tweets. Nowadays, tweets are widely used for analyzing the sentiments of the users, and utilized for decision making purposes. Though clustering and classification methods are used for the twitter sentiment analysis, meta-heuristic based clustering methods has witnessed better performance due to subjective nature of tweets. However, sequential meta-heuristic based clustering methods are computation intensive for large scale datasets. Therefore, in this paper, a novel MapReduce based K-means biogeography based optimizer(MR-KBBO) is proposed to leverage the strength of biogeography based optimizer with MapReduce model to efficiently cluster the large scale data. The proposed method is validated against four state-of-the-art MapReduce based clustering methods namely; parallel K-means, parallel K-means particle swarm optimization, MapReduce based artificial bee colony optimization, dynamic frequency based parallel k-bat algorithm on four large scale twitter datasets. Further, speedup measure is used to illustrate the computation performance on varying number of nodes. Experimental results demonstrate that the proposed method is efficient in sentiment mining for the large scale twitter datasets.

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Conference papers on the topic "Dynamic K-means algorithm"

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Tan, Pengliu, Ruoxin Tu, Xue Li, and Peixin He. "Dynamic Scalable PBFT Consensus Algorithm Based on Binary K-Means." In 2025 11th International Symposium on System Security, Safety, and Reliability (ISSSR). IEEE, 2025. https://doi.org/10.1109/isssr65654.2025.00055.

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Lao, Yecheng, and Kaixuan Lin. "Hybrid Recommendation Algorithm Based on K-Means++ and Dynamic Time Weights." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653352.

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Bao, Ziyi, and Xinhua Wu. "Study on the Distribution Characteristics and Dynamic Evolution of Green Buildings in China Based on the K-means Algorithm." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010510.

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S, Sneha, Sriranjini S, Himasai Thupakula, and Balaji M. "An IoT-Enabled Intelligent Traffic Management System with CNN-Based Accident Detection and Dynamic Rerouting Using K-Means and Dijkstra's Algorithm." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894232.

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Hai, Yin, Bing Han, Wei Su, He Lin, and Biao Liu. "R - Tree Index Construction of Dynamic K-means Algorithm." In 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2018. http://dx.doi.org/10.1109/fskd.2018.8687189.

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Han, Li. "Using a Dynamic K-means Algorithm to Detect Anomaly Activities." In 2011 Seventh International Conference on Computational Intelligence and Security (CIS). IEEE, 2011. http://dx.doi.org/10.1109/cis.2011.233.

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Huan, Zhu, Zhang Pengzhou, and Gao Zeyang. "K-means Text Dynamic Clustering Algorithm Based on KL Divergence." In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). IEEE, 2018. http://dx.doi.org/10.1109/icis.2018.8466385.

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Kim, Dongmoon, Kun-su Kim, Kyo-Hyun Park, Jee-Hyong Lee, and Keon Myung Lee. "A music recommendation system with a dynamic k-means clustering algorithm." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.97.

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Sun, Yuancun, and Sanming Liu. "Dynamic equivalence for wind farm based on IBSA-K-means algorithm." In 2017 2nd International Conference on Power and Renewable Energy (ICPRE). IEEE, 2017. http://dx.doi.org/10.1109/icpre.2017.8390574.

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Dai, Wei, Siyuan Yang, Mondher Bouazizi, and Tomoaki Ohtsuki. "K-Means Clustering-Aided Dynamic Multi-Cell Optimization Algorithm for HAPS." In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437302.

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