Journal articles: '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.

Full text

Abstract:

Abstract 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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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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Mostafa, Samih M., and Hirofumi Amano. "Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique." Applied Sciences 10, no. 15 (2020): 5134. http://dx.doi.org/10.3390/app10155134.

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Minimizing time cost in time-shared operating system is the main aim of the researchers interested in CPU scheduling. CPU scheduling is the basic job within any operating system. Scheduling criteria (e.g., waiting time, turnaround time and number of context switches (NCS)) are used to compare CPU scheduling algorithms. Round robin (RR) is the most common preemptive scheduling policy used in time-shared operating systems. In this paper, a modified version of the RR algorithm is introduced to combine the advantageous of favor short process and low scheduling overhead of RR for the sake of minimizing average waiting time, turnaround time and NCS. The proposed work starts by clustering the processes into clusters where each cluster contains processes that are similar in attributes (e.g., CPU service period, weights and number of allocations to CPU). Every process in a cluster is assigned the same time slice depending on the weight of its cluster and its CPU service period. The authors performed comparative study of the proposed approach and popular scheduling algorithms on nine groups of processes vary in their attributes. The evaluation was measured in terms of waiting time, turnaround time, and NCS. The experiments showed that the proposed approach gives better results.

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Sindhwani, Manoj, Charanjeet Singh, and Rajeshwar Singh. "Implementation of K-Means Algorithm and Dynamic Routing Protocol in VANET." Computer Systems Science and Engineering 40, no. 2 (2022): 455–67. http://dx.doi.org/10.32604/csse.2022.018498.

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Yang, Xu, Xiao Rong Chen, and Yi Ting Li. "Research on Dynamic K-Means Clustering Algorithm in Cyanobacteria Blooms Detection." Applied Mechanics and Materials 157-158 (February 2012): 428–32. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.428.

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Cyanobacteria blooms are constantly observed in the coastal waters and pose an enormous threat to public health, economy and ecological environment. The characteristics of blue algal bloom images and feature extraction procedures are analyzed in this paper. The pixel value of Cyanobacteria blooms color images has a significant difference from normal coastal waters images, particularly those of Hue and Saturation. A new method is proposed for Cyanobacteria blooms detection using dynamic K-means algorithm. Experimental results demonstrate the excellent practicability of the proposed detection method. Based on the pixel statistics, it can achieve a highly successful probability of detecting bloom images. Therefore, the proposed detection method can be expected to classify and detect Cyanobacteria blooms in monitoring and forecasting systems.

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Chinrungrueng, C., and C. H. Sequin. "Optimal adaptive k-means algorithm with dynamic adjustment of learning rate." IEEE Transactions on Neural Networks 6, no. 1 (1995): 157–69. http://dx.doi.org/10.1109/72.363440.

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Selvaraj, Suganya, and Eunmi Choi. "Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering." Sensors 22, no. 24 (2022): 9653. http://dx.doi.org/10.3390/s22249653.

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Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm.

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Fu, Liu Qiang, and Hong Wei Zhang. "Dynamic Clustering Based on Quantum-Behaved Particle Swarm Optimization." Advanced Materials Research 798-799 (September 2013): 808–13. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.808.

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Most clustering algorithm require the number of cluster as a priori knowledge to input, and metrics based on Euclidean distance is good results with only circular clusters. An improved dynamic clustering algorithm was presented, which combines the quantum particle swarm algorithm with k-means algorithm by improving the encoding of quantum particles and the introduction of new distance metric rules. The algorithm has a quantum-behaved particle swarm global search capability. And In order to accelerate the convergence speed, the k-means algorithm is used to optimize every particle .Through the adjustment of the value of the fitness function, our algorithm can search for the optimal clustering number of clusters, so the number of clusters and centers are not subject to subjective factors. Extensive experiments verified the effectiveness of the algorithm.

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Bhoomi, Mansukhlal Bangoria, S. Panchal Sweta, R. Panchal Sandipkumar, and Mansukhbhai Bangoria Tusharkumar. "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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Abstract:

Abstract 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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Han, Ping, and Jia Jia Chai. "The Application of K-Means in Personal Credit Analysis." Advanced Materials Research 403-408 (November 2011): 2461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2461.

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In this paper, we use K-means algorithm to realize dynamic clustering for the information of personal credit card bills, and through the statistical analysis of the results, we can get the analytics of the general trend of consumer behavior completely. In the experiment, we use K-means Algorithm to implement the clustering and analyze the experimental results which show that the results of this clustering can play a certain role in the analysis of the credit habit of discreditable users and high quality ones.

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Zeng, Lang, Zhen Jia, and Yingying Wang. "A new spectral coarse-graining algorithm based on K-means clustering in complex networks." Modern Physics Letters B 33, no. 01 (2019): 1850421. http://dx.doi.org/10.1142/s0217984918504213.

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Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

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Pierre, Ahkeim, Enrico Zio, Jessica Brown, and Louis Petit. "Multi-group sparrow search algorithm based on K-means clustering." Journal of Applied Artificial Intelligence 1, no. 1 (2024): 105–27. http://dx.doi.org/10.59782/aai.v1i1.281.

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In order to improve the defects of the Sparrow Search Algorithm (SSA) in single-species search, which causes redundancy in its speed of collection and easily ignores high-quality solutions and falls into local optimality, a K-means Multigroup Sparrow Search Algorithm (KSSA) based on K-means clustering was proposed. Firstly, the multi-population mechanism is introduced into SSA to weaken the collection ability of a single population and reduce the probability of falling into the local optimum. Secondly, the K-means algorithm is used to divide the sub-populations to increase the differences between the sub-populations, and at the same time, the individuals in the sub-populations focus on searching in a small range, thereby improving the efficiency of the initial search. Then, the weighted centroid communication strategy is used to improve the quality of communication between populations, reduce the interference of the own population, and reduce the risk of all sub-populations falling into the local optimum due to a sub-population falling into the local optimum. Finally, dynamic reverse learning is introduced into the vigilant to enhance its anti-predator behavior and improve the defects of slower collection speed and insufficient collection accuracy caused by the increase in the number of sub-populations. The test function simulation experiment shows that KSSA has better optimization performance than SSA and other algorithms.

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Mursalim, Mursalim, Purwanto Purwanto, and M. Arief Soeleman. "Penentuan Centroid Awal Pada Algoritma K-Means Dengan Dynamic Artificial Chromosomes Genetic Algorithm Untuk Tuberculosis Dataset." Techno.Com 20, no. 1 (2021): 97–108. http://dx.doi.org/10.33633/tc.v20i1.4230.

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Data merupakan hal penting diera sekarang begitu juga dengan metode data mining yang dapat mengekstraksi data menghasilkan informasi. Klastering 1 dari 5 peran data mining yang berfungsi untuk mengelompokkan data berdasarkan tingkat kemiripan dan jarak minimum. Algoritma K-Means termasuk algoritma yang populer banyak digunakan diberbagai bidang seperti bidang pendidikan, kesehatan, sosial, biologi, ilmu komputer. Seringkali metode K-Means dikombinasikan dengan metode optimasi seperti algoritma genetika untuk mengatasi permasalah pada K-Means yaitu sensitif dalam penentuan centroid awal .Namun metode algoritma genetika memiliki kekurangan yaitu mengalamai konvergen prematur sehingga hasil dari algorima genetika terjebak pada optimum lokal. Penelitian ini mengkombinasikan dynamic artificial cromosomes genetic algorithm dengan K-Means dalam menentukan nilai centroid awal pada k-means. Hasil eksperimen menunjukkan bahwa metode DAC GA + K-Means lebih unggul dibandingkan dengan K-Means dan GA + K-Means pada 2 dataset yang diuji dengan optimal nilai klaster sebanyak 2 dan 1 dataset sebanyak 3 klaster. Metode tersebut perolehan nilai DBI sebesar 0.138, 0.279 serta 0.382, nilai Sum Square Error sebesar 92.56, 332,39 dan 1280.68 serta nilai fitness yang tebentuk adalah 7.12, 3.57 dan 2.13.

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Hossain, Md Zakir, Md Nasim Akhtar, R. B. Ahmad, and Mostafijur Rahman. "A dynamic K-means clustering for data mining." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (2019): 521. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp521-526.

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Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets. The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed.The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.

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Hossain, Md. Zakir, Md. Nasim Akhtar, R.B. Ahmad, and Mostafijur Rahman. "A dynamic K-means clustering for data mining." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (2019): 521–26. https://doi.org/10.11591/ijeecs.v13.i2.pp521-526.

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Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets. The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed. The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of KMeans and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.

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Shirazi, Zahra Zandieh, and Seid Javad Mirabedini. "Dynamic K-Means Algorithm for Optimized Routing in Mobile Ad Hoc Networks." International Journal of Computer Science & Engineering Survey 7, no. 2 (2016): 01–14. http://dx.doi.org/10.5121/ijcses.2016.7201.

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Li, Haiyang, Hongzhou He, and Yongge Wen. "Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation." Optik 126, no. 24 (2015): 4817–22. http://dx.doi.org/10.1016/j.ijleo.2015.09.127.

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Buaton, Relita, and Solikhun Solikhun. "The Application of Numerical Measure Variations in K-Means Clustering for Grouping Data." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 1 (2023): 103–12. http://dx.doi.org/10.30812/matrik.v23i1.3269.

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The K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clustering. The purpose of this research was to compare distance calculation methods with K-Means such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Similarity, Dynamic TimeWarping Distance, Jaccard Similarity, and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. The best distancecalculation was determined from the smallest Davies Bouldin Index value. This research aimed to find optimal clusters using the K-Means Clustering algorithm with seven distance calculations based on types of numerical measures. This research method compared distance calculation methods in the K-Means algorithm, such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Smilirity, Dynamic Time Warping Distance, Jaccard Smilirity and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. Determining the best distance calculation can be seen from the smallest Davies Bouldin Index value. The data used in this study was on cosmetic sales at Devi Cosmetics, consisting of cosmetics sales from January to April 2022 with 56 product items. The result of this study was a comparison of numerical measures in the K-Means Clustering algorithm. The optimal cluster was calculating the Euclidean distance with a total of 9 clusters with a DBI value of 0.224. In comparison, the best average DBI value was the calculation of the Euclidean Distance with an average DBI value of 0.265.

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Tang, Jin Yan, Yue Lei Xie, and Cheng Cheng Peng. "A Dynamic Sub-Array Divided Technique for Spherical Conformal Array Antenna Using K-Means Algorithm." Advanced Materials Research 926-930 (May 2014): 2884–88. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2884.

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In this paper, a sub-array divided technique using K-means algorithm for spherical conformal array is proposed. All elements of spherical conformal array can be divided into a few sub-arrays by employing the K-means algorithm, and the standard multiple signal classification (MUSIC) algorithm is applied to estimate signals Direction-of-arrival (DOA) on these sub-arrays. Simulations of estimating DOA on a rotational spherical conformal array have been made and the results show that the resolution of DOA is improved by our method compare to existing methods.

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Zhu, Minchen, Weizhi Wang, and Jingshan Huang. "Improved initial cluster center selection in K-means clustering." Engineering Computations 31, no. 8 (2014): 1661–67. http://dx.doi.org/10.1108/ec-11-2012-0288.

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Purpose – It is well known that the selection of initial cluster centers can significantly affect K-means clustering results. The purpose of this paper is to propose an improved, efficient methodology to handle such a challenge. Design/methodology/approach – According to the fact that the inner-class distance among samples within the same cluster is supposed to be smaller than the inter-class distance among clusters, the algorithm will dynamically adjust initial cluster centers that are randomly selected. Consequently, such adjusted initial cluster centers will be highly representative in the sense that they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can then be effectively reduced. In addition, the algorithm is able to obtain all initial cluster centers simultaneously (instead of one center at a time) during the dynamic adjustment. Findings – Experimental results demonstrate that the proposed algorithm greatly improves the accuracy of traditional K-means clustering results and, in a more efficient manner. Originality/value – The authors presented in this paper an efficient algorithm, which is able to dynamically adjust initial cluster centers that are randomly selected. The adjusted centers are highly representative, i.e. they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can be effectively reduced so that the authors can achieve an improved clustering accuracy. In addition, the algorithm is a cost-efficient one and the enhanced clustering accuracy can be obtained in a more efficient manner compared with traditional K-means algorithm.

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Siregar, Bakti, and Yosia Yosia. "Implementation of K-means Clustering Algorithm for the Indonesian Stock Exchange." JURNAL SISFOTEK GLOBAL 14, no. 1 (2024): 49. http://dx.doi.org/10.38101/sisfotek.v14i1.10860.

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In the dynamic field of financial markets, effective analysis and understanding of stock market behavior are very crucial for investors, analysts, and policymakers. This study investigates the implementation of the K-means algorithm for clustering stocks listed on the Indonesian Stock Exchange (IDX). The main objectives of this research include exploring IDX's clustering patterns, identifying groups based on their trading characteristics, and evaluating algorithm performance. Some challenging parts have been addressed, such as data quality, feature selection, determining the optimal number of clusters, scalability, interpretability, and evaluation. Precise data preprocessing, feature engineering, and algorithm optimization provide insight into the clustering structure of the Indonesian stock market, helping investors in portfolio diversification, risk management, and strategic decision-making. The results show the potential of the K-means algorithm in thoroughly uncovering important patterns on the IDX, thereby contributing to the advancement of market analysis methodologies adapted to the Indonesian financial environment.

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Xue, Feng, Yongbo Liu, Xiaochen Ma, Bharat Pathak, and Peng Liang. "A hybrid clustering algorithm based on improved GWO and KHM clustering." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 3227–40. http://dx.doi.org/10.3233/jifs-211034.

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To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.

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Bataineh, Asia K., Mohammad Habib Samkari, Abdualla Abdualla, and Saad Al-Azzam. "K-Means Clustering in WSN with Koheneon SOM and Conscience Function." Modern Applied Science 13, no. 8 (2019): 63. http://dx.doi.org/10.5539/mas.v13n8p63.

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Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map&nbsp;(KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

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Kumari, Roopa, Neena Gupta, and Narender Kumar. "Segmentation of Covid-19 Affected X-Ray Image using K-means and DPSO Algorithm." International Journal of Mathematical, Engineering and Management Sciences 6, no. 5 (2021): 1255–75. http://dx.doi.org/10.33889/ijmems.2021.6.5.076.

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Covid-19, a disease that originated in the Chinese city of Wuhan, has spread across almost the entire globe. Pneumonia, which infects the lungs, is one of the symptoms of this disease. In the past X-ray images were used to segment various diseases such as pneumonia, tuberculosis, or lung cancer. Recent studies showed that Covid-19 affects the lungs. As a result, an X-ray imaging could help to detect and diagnose Covid-19 infection. This study presents a novel hybrid algorithm (CHDPSOK) for segmenting a Covid-19 infected X-ray image. To find Covid-19 contamination in the lungs, we use a segmentation-based approach using K-means and Dynamic PSO algorithm. In the present paper, segmentation of infected regions in the X-ray image uses a cumulative histogram to initialize the population of the PSO algorithm. In a dynamic PSO algorithm, the velocity of the particle changes dynamically which is useful to avoid the local minima. K-means is used to change the position of the particle dynamically for better convergence. To validate the segmentation performance of our algorithm, we used the Kaggle dataset in our experiments. The performance of the proposed method is analyzed both qualitatively and quantitatively. The results explicitly demonstrate the outperformance of the proposed algorithm.

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Sulaiman, Hamdun, Yuri Yuliani, Kukuh Panggalih, M. Iqbal Alifudin, and Kudiantoro Widianto. "Pengelompokan Keaktifan Anggota Perpustakaan Menggunakan Algoritma K-Means." Infotek: Jurnal Informatika dan Teknologi 8, no. 1 (2025): 56–65. https://doi.org/10.29408/jit.v8i1.27978.

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This study aims to analyze membership activity at the Rimba Baca Library in South Jakarta using the K-Means algorithm. The background of this research is the library's need to understand membership patterns and improve services based on visit and book borrowing data. The dataset for this study consists of 81 membership records collected from 2023 to 2024. The methodology involved collecting visit and book borrowing data, then applying the K-Means algorithm to cluster members based on their activity levels. The results of the study indicate the presence of three clusters with different characteristics. Cluster 1 comprises very active members, while Clusters 0 and 2 exhibit lower levels of activity. These findings provide insights for the library to develop more effective service strategies, such as special promotions and programs to increase activity among less active member groups. Additionally, the study shows that membership types allowing for more book borrowings do not necessarily correlate with high activity levels. With this information, the library can enhance member engagement and optimize the use of existing resources, thereby creating a more dynamic and interactive environment for all visitors

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Mensah, Patrick Kwabena, Benjamin Asubam Weyori, and Mighty Abra Ayidzoe. "Capsule network with K-Means routingfor plant disease recognition." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 1025–36. http://dx.doi.org/10.3233/jifs-201226.

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Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets.

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Guo, Chonghui, Mucan Liu, and Menglin Lu. "A Dynamic Ensemble Learning Algorithm based on K-means for ICU mortality prediction." Applied Soft Computing 103 (May 2021): 107166. http://dx.doi.org/10.1016/j.asoc.2021.107166.

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Xiaoqiong, Wei, and Yin E. Zhang. "Image segmentation algorithm based on dynamic particle swarm optimization and K-means clustering." International Journal of Computers and Applications 42, no. 7 (2018): 649–54. http://dx.doi.org/10.1080/1206212x.2018.1521090.

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C., Avinash, Hasritha P., and Abhinay J. "A Dynamic K-means Algorithm for Searching Conserved Encrypted Data in a Cloud." International Journal of Computer Applications 129, no. 5 (2015): 33–38. http://dx.doi.org/10.5120/ijca2015906918.

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Dong, Dianbiao, Yahui Zhu, Zhize Du, and Dengxiu Yu. "Multi-target dynamic hunting strategy based on improved K-means and auction algorithm." Information Sciences 640 (September 2023): 119072. http://dx.doi.org/10.1016/j.ins.2023.119072.

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Li, Ning, Yunxia Gu, and Zhongliang Deng. "Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5707692.

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A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

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Y., Hamzaoui, Amnai M., Choukri A., and Fakhri Y. "Enhancenig OLSR routing protocol using K-means clustering in MANETs." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3715–24. https://doi.org/10.11591/ijece.v10i4.pp3715-3724.

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The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method to distribute the nodes into several clusters; it is implemented to standard OLSR protocol giving birth a new protocol named OLSR Kmeans-SDE. The simulations showed that the results obtained by OLSR Kmeans-SDE exceed those obtained by standard OLSR Kmeans and OLSR Kmed+ in terms of, traffic Control, delay and packet delivery ratio.

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Tao, Dan. "Dynamic Web Page Graphic Design Method for Internet Big Data Information System." Mathematical Problems in Engineering 2022 (August 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/6753671.

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With the rapid development of the Internet, the page design technology of PC search engine tends to be mature. The content displayed in front of users not only pays attention to the practicability of information, but also takes into account the beauty of page layout. However, the web page design of the Internet mobile terminal is slightly backward, so we study the multimodal method based on the feature of large data density to extract the text, and use the K-means clustering algorithm to classify and recognize the text. The results show that the comprehensive evaluation of the design search engine on multiple platforms is higher than 0.89; In fuzzy keyword retrieval, the average accuracy of dynamic k-value clustering algorithm is 87.5%, while the average accuracy of traditional K-means clustering algorithm is 78.5%. Finally, in terms of user evaluation, the satisfaction of search pages increased by 5%–10%. Experiments show that the optimized algorithm and page design not only improve the accuracy and applicability in function, but also optimize the layout of text and pictures on the page.

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Han, Dezhi, Kun Bi, Han Liu, and Jianxin Jia. "A DDoS attack detection system based on spark framework." Computer Science and Information Systems 14, no. 3 (2017): 769–88. http://dx.doi.org/10.2298/csis161217028h.

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There are many problems in traditional Distributed Denial of Service (DDoS) attack detection such as low accuracy, low detection speed and so on, which is not suitable for the real time detecting and processing of DDoS attacks in big data environment. This paper proposed a novel DDoS attack detection system based on Spark framework including 3 main algorithms. Based on information entropy, the first one can effectively warn all kinds of DDoS attacks in advance according to the information entropy change of data stream source IP address and destination IP address; With the help of designed dynamic sampling K-Means algorithm, this new detection system improves the attack detection accuracy effectively; Through running dynamic sampling K-Means parallelization algorithm, which can quickly and effectively detect a variety of DDoS attacks in big data environment. The experiment results show that this system can not only early warn DDoS attacks effectively, but also can detect all kinds of DDoS attacks in real time, with low false rate.

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Xu, Huipu, and Yiteng Wang. "Target Detection Network for Underwater Image Based on Adaptive Anchor Frame and Re-parameterization." Journal of Physics: Conference Series 2363, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2363/1/012012.

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For low target detection rate and inaccurate localization of most target detection networks in special underwater environments, we propose an underwater target detection algorithm with adaptive anchor frames. The adaptive anchor frame selection strategy differs from the traditional methods of manually designing anchor frames and obtaining anchor frames using the K-means clustering algorithm. Our method is based on the K-means clustering algorithm and consists of a grouping strategy and a dynamic K-value strategy. It can also automatically calculate the appropriate numbers of anchor boxes and layers of network output based on the characteristics of label data. And performance of the network is guaranteed while avoiding the computation caused by redundant structures. The simple but powerful RepVGG classification network is used as the backbone network. Therefore, the feature extraction capability of the network is enhanced and the detection performance of the network on fuzzy targets is improved. The experimental results show that the algorithm in this paper achieves better detection results in the underwater target detection task compared to other algorithms.

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Hamdani, Mostefa, and Youcef Aklouf. "Enhanced active VM load balancing algorithm using fuzzy logic and K-means clustering." Multiagent and Grid Systems 17, no. 1 (2021): 59–82. http://dx.doi.org/10.3233/mgs-210343.

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With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual machine cost, the data center processing time and the response time. The proposed method is realized using Java and CloudAnalyst Simulator. Besides, we have compared the proposed algorithm with other task scheduling approaches such as Round Robin algorithm, Throttled algorithm, Equally Spread Current Execution Load algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). As a result, the proposed algorithm performs better in terms of service rate and response time.

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Xu, Xingchen, Xingguang Geng, Zhixing Gao, Hao Yang, Zhiwei Dai, and Haiying Zhang. "Optimal Heart Sound Segmentation Algorithm Based on K-Mean Clustering and Wavelet Transform." Applied Sciences 13, no. 2 (2023): 1170. http://dx.doi.org/10.3390/app13021170.

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The accurate localization of S1 and S2 is essential for heart sound segmentation and classification. However, current direct heart sound segmentation algorithms have poor noise immunity and low accuracy. Therefore, this paper proposes a new optimal heart sound segmentation algorithm based on K-means clustering and Haar wavelet transform. The algorithm includes three parts. Firstly, this method uses the Viola integral method and Shannon’s energy-based algorithm to extract the function of the envelope of the heart sound energy. Secondly, the time–frequency domain features of the acquired envelope are extracted from different dimensions and the optimal peak is searched adaptively based on a dynamic segmentation threshold. Finally, K-means clustering and Haar wavelet transform are implemented to localize S1 and S2 of heart sounds in the time domain. After validation, the recognition rate of S1 reached 98.02% and that of S2 reached 96.76%. The model outperforms other effective methods that have been implemented. The algorithm has high robustness and noise immunity. Therefore, it can provide a new method for feature extraction and analysis of heart sound signals collected in clinical settings.

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Gendre, Vinod. "Efficient Crime Analysis Based on Hybrid Approach by Combining Dynamic Time Wrapping Algorithm with K-Means Clustering Approach." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4394–401. http://dx.doi.org/10.22214/ijraset.2022.44846.

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Abstract: Crime is a preeminent issue where the main concern has been worried by individual, the local area and government. Wrongdoing forecast utilizes past information and in the wake of investigating information, anticipate the future wrongdoing with area and time. In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution. Clustering different time series into similar groups is a challenging clustering task because each data point is an ordered sequence. The most common approach to time series clustering is to flatten the time series into a table, with a column for each time index (or aggregation of the series) and directly apply standard clustering algorithms like k-means. But this doesn’t always work well on Time Series Data. The paper focuses on combining the features of K-Means Clustering algorithm with Dynamic Time Wrapping Algorithm for efficient Crime prediction and analysis

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Pulhani, Yashu, Ankur Singh Kang, and Vishal Sharma. "Performance analysis on self organization based clustering scheme for FANETs using K-means algorithm and firefly optimization." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 2 (2022): 148. http://dx.doi.org/10.11591/ijict.v11i2.pp148-159.

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With the fast-increasing development of wireless communication networks, unmanned aerial vehicle (UAV) has emerged as a flying platform for wireless communication with efficient coverage, capacity, reliability, and its network is called flying ad-hoc network (FANET); which keeps changing its topology due to its dynamic nature, causing inefficient communication, and therefore needs cluster formation. In this paper, we proposed a cluster formation, selection of cluster head and its members, connectivity and transmission with the base station using the K-means algorithm, and choice of an optimized path for transmission using firefly optimization algorithm for efficient communication. Evaluation of performance with experimental results are obtained and compared using the K-means algorithm and firefly optimization algorithm in cluster building time, cluster lifetime, energy consumption, and probability of delivery success. On comparison of firefly optimization algorithm with firefly optimization algorithm, i.e., K-means algorithm results proved than without firefly optimization algorithm, better in terms of cluster building time, energy consumption, cluster lifetime, and also the probability of delivery success.

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Yashu, Pulhani, Singh Kang Ankur, and Sharma Vishal. "Performance analysis on self organization based clustering scheme for FANETs using K-means algorithm and firefly optimization." International Journal of Informatics and Communication Technology 11, no. 2 (2022): 148–59. https://doi.org/10.11591/ijict.v11i2.pp148-159.

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With the fast-increasing development of wireless communication networks, unmanned aerial vehicle (UAV) has emerged as a flying platform for wireless communication with efficient coverage, capacity, reliability, and its network is called flying ad-hoc network (FANET); which keeps changing its topology due to its dynamic nature, causing inefficient communication, and therefore needs cluster formation. In this paper, we proposed a cluster formation, selection of cluster head and its members, connectivity and transmission with the base station using the K-means algorithm, and choice of an optimized path for transmission using firefly optimization algorithm for efficient communication. Evaluation of performance with experimental results are obtained and compared using the K-means algorithm and firefly optimization algorithm in cluster building time, cluster lifetime, energy consumption, and probability of delivery success. On comparison of firefly optimization algorithm with firefly optimization algorithm, i.e., K-means algorithm results proved than without firefly optimization algorithm, better in terms of cluster building time, energy consumption, cluster lifetime, and also the probability of delivery success.

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M., Rakesh Chowdary, Yashwanth Reddy A., and Abhishek N. "RESOURCE MANAGEMENT IN DEALING WITH SECURITY CHALLENGES IN CLOUD BASED ENVIRONMENT." International Journal of Applied and Advanced Scientific Research 1, no. 2 (2016): 152–55. https://doi.org/10.5281/zenodo.1034457.

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Cloud Service Brokers includes technology consultants, business professional service organizations, registered brokers and agents, and influencers that help guide consumers in the selection of cloud computing solutions. Service brokers concentrate on the negotiation of the relationships between consumers and providers without owning or managing the whole Cloud infrastructure. Moreover, they add extra services on top of a Cloud provider’s infrastructure to make up the user’s Cloud environment.With the emergence of many new data centers around the globe; energy consumption by those data centers has been tremendously increased. Dynamic capacity provisioning is a promising approach for reducing energy consumption by dynamically adjusting the number of active machines to match resource demands.

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Liu, Lisang, Jiangfeng Guo, and Rongsheng Zhang. "YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments." Electronics 11, no. 18 (2022): 2872. http://dx.doi.org/10.3390/electronics11182872.

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Visual simultaneous localization and mapping (SLAM) algorithms in dynamic scenes can incorrectly add moving feature points to the camera pose calculation, which leads to low accuracy and poor robustness of pose estimation. In this paper, we propose a visual SLAM algorithm based on object detection and static probability update strategy for dynamic scenes, named YKP-SLAM. Firstly, we use the YOLOv5 target detection algorithm and the improved K-means clustering algorithm to segment the image into static regions, suspicious static regions, and dynamic regions. Secondly, the static probability of feature points in each region is initialized and used as weights to solve for the initial camera pose. Then, we use the motion constraints and epipolar constraints to update the static probability of the feature points to solve the final pose of the camera. Finally, it is tested on the TUM RGB-D dataset. The results show that the YKP-SLAM algorithm proposed in this paper can effectively improve the pose estimation accuracy. Compared with the ORBSLAM2 algorithm, the absolute pose estimation accuracy is improved by 56.07% and 96.45% in low dynamic scenes and high dynamic scenes, respectively, and the best results are almost obtained compared with other advanced dynamic SLAM algorithms.

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

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