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Relevant bibliographies by topics / Weighted Clustering Algorithm

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Academic literature on the topic 'Weighted Clustering Algorithm'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Weighted Clustering Algorithm"

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Ackerman, Margareta, Shai Ben-David, Simina Brânzei, and David Loker. "Weighted Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 858–63. http://dx.doi.org/10.1609/aaai.v26i1.8282.

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We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights. We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classif

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Cufoglu, Ayse, Mahi Lohi, and Colin Everiss. "Feature weighted clustering for user profiling." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 04 (2017): 1750056. http://dx.doi.org/10.1142/s1793962317500568.

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Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (I

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Qian, Yue, Shixin Yao, Tianjun Wu, You Huang, and Lingbin Zeng. "Improved Selective Deep-Learning-Based Clustering Ensemble." Applied Sciences 14, no. 2 (2024): 719. http://dx.doi.org/10.3390/app14020719.

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Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstru

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Yang, Ying, Haoyu Chen, and Haoshen Wu. "A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning." PeerJ Computer Science 9 (October 5, 2023): e1600. http://dx.doi.org/10.7717/peerj-cs.1600.

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Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers’ influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evalu

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Chen, JunYing, Zheng Qin, and Ji Jia. "A Weighted Mean Subtractive Clustering Algorithm." Information Technology Journal 7, no. 2 (2008): 356–60. http://dx.doi.org/10.3923/itj.2008.356.360.

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Wang, Hengda, Mohamad Farhan Mohamad Mohsin, and Muhammad Syafiq Mohd Pozi. "Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering." Journal of Information and Communication Technology 23, no. 3 (2024): 523–59. http://dx.doi.org/10.32890/jict2024.23.3.6.

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The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm u

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Li, Qiang. "A Spectrum Clustering Algorithm Based on Weighted Fuzzy Similar Matrix." Advanced Materials Research 482-484 (February 2012): 2109–13. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2109.

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Unlike those traditional clustering algorithms, the spectral clustering algorithm can be applied to non-convex sphere of sample spaces and be converged to global optimal. As a entry point that the similar of spectral clustering, introduce improved weighted fuzzy similar matrix to spectral in this paper which avoids influence from parameters changes of fuzzy similar matrix in traditional spectral clustering on clustering effect and improves the effectiveness of clustering. It is more actual and scientific, which is tested based on UCI data set.

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Yang, Hua, and Zhi-mei Li. "A Genetic-algorithm-based Weighted Clustering Algorithm in MANET." International Journal of Future Generation Communication and Networking 10, no. 3 (2017): 31–40. http://dx.doi.org/10.14257/ijfgcn.2017.10.3.04.

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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 th

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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 l

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Dissertations / Theses on the topic "Weighted Clustering Algorithm"

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Chan, Yat-ling, and 陳逸靈. "An optimization algorithm for clustering using weighted dissimilarity measures." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26667009.

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Zhou, Yufeng. "Performance Evaluation of a Weighted Clustering Algorithm in NSPS Scenarios." Thesis, KTH, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140427.

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In national security and public safety (NSPS) scenarios, the concept of device-to-device (D2D) clustering allows user equipment (UEs) to dynamically form clusters and thereby allows for local communication with partial or no cellular network assistance. We propose and evaluate a clustering approach to solve this problem in this thesis report. One of the key components of clustering is the selection of so called cluster head (CH) nodes that are responsible for the formation of clusters and act as a synchronization and radio resource management information source. In this thesis work we propose

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Masum, Mohammad. "Vertex Weighted Spectral Clustering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3266.

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Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the v

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Fan, Yi. "Advancing Incomplete Algorithms for Maximum Weight Cliques." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/371136.

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Computing MaximumWeight Cliques (MWC) has many applications in data mining and computer vision including solving graphical models. Among various heuristic methods for MWC, local search is an e ective approach. The local search approach tries to improve the candidate clique by adding, dropping or swapping vertices. It is able to return good solutions in reasonable time periods. In this thesis, we use local search to solve the MWC problem on crafted instances, and then apply the algorithms to solve real-world problems. When we are solving real-world problems, we have to rst model them as an MWC

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Prego, Lilach. "Algorithm for directed graph clustering, based on edge weights and the implementation on web graphs /." [S.l.] : [s.n.], 2005. http://lib.haifa.ac.il/theses/general/001344252.pdf.

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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.

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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of th

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Biswal, Suryadeep, and Dimol Soren. "Study of different mobility models and clustering algorithms like weighted clustering algorithm (WCA) and dynamic moblity adaptive clustering algorithm (DMAC)." Thesis, 2007. http://ethesis.nitrkl.ac.in/4211/1/Study_of_different_mobility_models_and_clustering_algorithms_like_Weighted_Clustering_Algorithm_(WCA)_and_Dynamic_Mobility_Adaptive_Clustering_Algorithm_(DMAC).pdf.

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This project addresses issues pertaining to mobile multi-hop radio networks called mobile ad hoc networks (MANET), which plays a critical role in places where a wired backbone is neither available nor economical to deploy. Our objective was to form and maintain clusters for efficient routing, scalability and energy utilization. To map the cellular architecture into the mobile ad hoc network cluster heads are elected that form the virtual backbone for packet transmission. However, the constant movement of the nodes changes the topology of the network, which perturbs the transmission. This deman

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Pereira, Luís Miguel Azevedo. "TweeProfiles4: a weighted multidimensional stream clustering algorithm." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/83533.

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O aparecimento das redes sociais abriu aos utilizadores a possibilidade de facilmente partilharem as suas ideias a respeito de diferentes temas, o que constitui uma fonte de informação enriquecedora para diversos campos. As plataformas de microblogging sofreram um grande crescimento e de forma constante nos últimos anos. O Twitter é o site de microblogging mais popular, tornando-se uma fonte de dados interessante para extração de conhecimento. Um dos principais desafios na análise de dados provenientes de redes sociais é o seu fluxo, o que dificulta a aplicação de processos tradicionais de dat

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Pereira, Luís Miguel Azevedo. "TweeProfiles4: a weighted multidimensional stream clustering algorithm." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/83533.

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O aparecimento das redes sociais abriu aos utilizadores a possibilidade de facilmente partilharem as suas ideias a respeito de diferentes temas, o que constitui uma fonte de informação enriquecedora para diversos campos. As plataformas de microblogging sofreram um grande crescimento e de forma constante nos últimos anos. O Twitter é o site de microblogging mais popular, tornando-se uma fonte de dados interessante para extração de conhecimento. Um dos principais desafios na análise de dados provenientes de redes sociais é o seu fluxo, o que dificulta a aplicação de processos tradicionais de dat

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Ye, Yanping, and 葉彥憑. "A Moving Pattern-Based Weighted Clustering Algorithm for VANETs." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/8p77cw.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>100<br>Clustering of vehicles in a VANET can reduce the communication traffic information among vehicles. With an effective clustering algorithm, a cluster leader is responsible for detecting malicious nodes and broadcasting traffic information for the members of the same group. However, due to the high mobility of vehicles and the short linkage time between vehicles, forming stable clusters in a VANET remains a challenging problem. In this paper, we propose a Moving Pattern-based weighted Clustering Algorithm, termed MPCA, for VANETs. The MPCA algorithm consists of

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Books on the topic "Weighted Clustering Algorithm"

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Schreiber, Thomas. A voronoi diagram based adaptive k-means-type clustering algorithm for multidimensional weighted data. Universität Kaiserslautern, 1991.

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Book chapters on the topic "Weighted Clustering Algorithm"

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Bao, Zhiqiang, Bing Han, and Shunjun Wu. "A General Weighted Fuzzy Clustering Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11867661_10.

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Li, Jie, Xinbo Gao, and Licheng Jiao. "A New Feature Weighted Fuzzy Clustering Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11548669_43.

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Dahane, Amine, and Nasr-Eddine Berrached. "A Distributed and Safe Weighted Clustering Algorithm." In Mobile, Wireless and Sensor Networks. Apple Academic Press, 2019. http://dx.doi.org/10.1201/9781351190756-5.

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Huang, Dong, Chang-Dong Wang, and Jian-Huang Lai. "LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_17.

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Li, Cuixia, and Yingjun Tan. "A Weighted Fuzzy Clustering Algorithm Based on Density." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29390-0_34.

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Bista, Rabindra, and Ajaya Thapa. "Energy Distance Neighborhood Based Weighted Hierarchical Clustering Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40305-8_12.

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Klikowski, Jakub, and Robert Burduk. "Distance Metrics in Clustering and Weighted Scoring Algorithm." In Progress in Image Processing, Pattern Recognition and Communication Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81523-3_3.

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Bricard-Vieu, Vincent, and Noufissa Mikou. "Distributed Mobility Prediction-Based Weighted Clustering Algorithm for MANETs." In Information Networking. Convergence in Broadband and Mobile Networking. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30582-8_75.

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Rocha, Ana Maria A. C., M. Fernanda P. Costa, and Edite M. G. P. Fernandes. "A Simple Clustering Algorithm Based on Weighted Expected Distances." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91885-9_7.

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Nath, Ira, Rituparna Chaki, and Nabendu Chaki. "WACA: A New Weighted Adaptive Clustering Algorithm for MANET." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14493-6_29.

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Conference papers on the topic "Weighted Clustering Algorithm"

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Wang, Na, Meng Xiao, Zhongliang Zhao, and Yang Liu. "A Fast Weighted Clustering Algorithm for FANET." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683555.

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Yang, Chongyang, Hua Xu, Zidan Zhang, and Rui Li. "Density Peak Clustering Algorithm Improved by Weighted Similarity." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743522.

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Suo, Jinyi, Aosheng Xing, and Tonglei Sun. "Density Peaks Clustering Algorithm Based on Weighted Shared Nearest Neighbors." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762183.

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Hu, Hong, Xuejun Li, and Jing Liao. "Multi-View Ensemble Clustering Algorithm Weighted by Kernel Density Estimation." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906209.

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Zheng, Ruoqi, Qi Zhang, Xiangjun Xin, et al. "Directional Antenna UAVs Networking Algorithm Based on On-Demand-Weighted Clustering." In 2024 22nd International Conference on Optical Communications and Networks (ICOCN). IEEE, 2024. http://dx.doi.org/10.1109/icocn63276.2024.10648428.

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Hua, Chunfeng, and Jiang Xie. "A Weighted Granular-Ball Clustering Algorithm Based on Regularization and Entropy." In 2025 10th International Conference on Computer and Communication System (ICCCS). IEEE, 2025. https://doi.org/10.1109/icccs65393.2025.11069830.

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Li, Siqi, Xiang Gao, Yuhan Liu, Piao He, Jianing Ji, and Peng Gong. "Anti-Jamming Weighted Clustering Algorithm for UAV-Mounted Weapon Self-Organizing Network." In 2025 27th International Conference on Advanced Communications Technology (ICACT). IEEE, 2025. https://doi.org/10.23919/icact63878.2025.10936791.

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kaizhi Peng, zhongying Liu, and Lai Tu. "Weighted-clustering cooperative spectrum sensing algorithm." In 2012 7th International Symposium on Wireless and Pervasive Computing (ISWPC). IEEE, 2012. http://dx.doi.org/10.1109/iswpc.2012.6263663.

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Chen, Xiuguo, Wensheng Yin, Pinghui Tu, and Hengxi Zhang. "Weighted k-Means Algorithm Based Text Clustering." In 2009 International Symposium on Information Engineering and Electronic Commerce (IEEC). IEEE, 2009. http://dx.doi.org/10.1109/ieec.2009.17.

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Ren, Yazhou, Carlotta Domeniconi, Guoji Zhang, and Guoxian Yu. "A Weighted Adaptive Mean Shift Clustering Algorithm." In Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.91.

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Reports on the topic "Weighted Clustering Algorithm"

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algo

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