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Journal articles on the topic 'Weighted Clustering Algorithm'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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2

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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Xu, Jiucheng, Qinchen Hou, Kanglin Qu, Yuanhao Sun, and Xiangru Meng. "A Fast Weighted Fuzzy C-Medoids Clustering for Time Series Data Based on P-Splines." Sensors 22, no. 16 (2022): 6163. http://dx.doi.org/10.3390/s22166163.

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The rapid growth of digital information has produced massive amounts of time series data on rich features and most time series data are noisy and contain some outlier samples, which leads to a decline in the clustering effect. To efficiently discover the hidden statistical information about the data, a fast weighted fuzzy C-medoids clustering algorithm based on P-splines (PS-WFCMdd) is proposed for time series datasets in this study. Specifically, the P-spline method is used to fit the functional data related to the original time series data, and the obtained smooth-fitting data is used as the

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Sun, Xiaoqi, Wenxi Gao, and Yinong Duan. "MR Brain Image Segmentation Using a Fuzzy Weighted Multiview Possibility Clustering Algorithm with Low-Rank Constraints." Journal of Medical Imaging and Health Informatics 11, no. 2 (2021): 402–8. http://dx.doi.org/10.1166/jmihi.2021.3280.

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To expand the multiview clustering abilities of traditional PCM in increasingly complex MR brain image segmentation tasks, a fuzzy weighted multiview possibility clustering algorithm with low-rank constraints (LR-FW-MVPCM) is proposed. The LR-FW-MVPCM can effectively mine both the internal consistency and diversity of multiview data, which are two principles for constructing a multiview clustering algorithm. First, a kernel norm is introduced as a low-rank constraint of the fuzzy membership matrix among multiple perspectives. Second, to ensure the clustering accuracy of the algorithm, the view

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Zhao, Yang, Pei-hong Wang, Yi-guo Li, and Meng-yang Li. "Fuzzy weighted c-harmonic regressions clustering algorithm." Soft Computing 22, no. 14 (2017): 4595–611. http://dx.doi.org/10.1007/s00500-017-2642-3.

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Siminski, Krzysztof. "Fuzzy weighted C-ordered means clustering algorithm." Fuzzy Sets and Systems 318 (July 2017): 1–33. http://dx.doi.org/10.1016/j.fss.2017.01.001.

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Wang, Boyuan, Xuelin Liu, Baoguo Yu, Ruicai Jia, and Xingli Gan. "An Improved WiFi Positioning Method Based on Fingerprint Clustering and Signal Weighted Euclidean Distance." Sensors 19, no. 10 (2019): 2300. http://dx.doi.org/10.3390/s19102300.

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WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In add

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Wan, Renxia, Yuelin Gao, and Caixia Li. "Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets." International Journal of Data Warehousing and Mining 8, no. 4 (2012): 82–107. http://dx.doi.org/10.4018/jdwm.2012100104.

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Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that

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Zhang, Lei, and Lina Ge. "A clustering-based differential privacy protection algorithm for weighted social networks." Mathematical Biosciences and Engineering 21, no. 3 (2024): 3755–33. http://dx.doi.org/10.3934/mbe.2024166.

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<abstract> <p>Weighted social networks play a crucial role in various fields such as social media analysis, healthcare, and recommendation systems. However, with their widespread application and privacy issues have become increasingly prominent, including concerns related to sensitive information leakage, individual behavior analysis, and privacy attacks. Despite traditional differential privacy protection algorithms being able to protect privacy for edges with sensitive information, directly adding noise to edge weights may result in excessive noise, thereby reducing data utility.

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Miloudi, Salim, Yulin Wang, and Wenjia Ding. "An Improved Similarity-Based Clustering Algorithm for Multi-Database Mining." Entropy 23, no. 5 (2021): 553. http://dx.doi.org/10.3390/e23050553.

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Clustering algorithms for multi-database mining (MDM) rely on computing (n2−n)/2 pairwise similarities between n multiple databases to generate and evaluate m∈[1,(n2−n)/2] candidate clusterings in order to select the ideal partitioning that optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the n databases in one cluster or by returning n sing

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Pan, Xingguang, Lin Wang, Chengquan Huang, Shitong Wang, and Haiqing Chen. "A novel weighted fuzzy c-means based on feature weight learning." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 6149–67. http://dx.doi.org/10.3233/jifs-202779.

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In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may d

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20

Jia, Ziqi, and Ling Song. "Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient." Mathematical Problems in Engineering 2020 (July 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/5143797.

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The k-prototypes algorithm is a hybrid clustering algorithm that can process Categorical Data and Numerical Data. In this study, the method of initial Cluster Center selection was improved and a new Hybrid Dissimilarity Coefficient was proposed. Based on the proposed Hybrid Dissimilarity Coefficient, a weighted k-prototype clustering algorithm based on the hybrid dissimilarity coefficient was proposed (WKPCA). The proposed WKPCA algorithm not only improves the selection of initial Cluster Centers, but also puts a new method to calculate the dissimilarity between data objects and Cluster Center

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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

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22

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

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Abstract <strong>Objectives:</strong> Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. <strong>Methods:</strong> This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and sig

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23

Liu, Jing, Guisheng Liao, Jingwei Xu, Shengqi Zhu, Cao Zeng, and Filbert H. Juwono. "Unsupervised Affinity Propagation Clustering Based Clutter Suppression and Target Detection Algorithm for Non-Side-Looking Airborne Radar." Remote Sensing 15, no. 8 (2023): 2077. http://dx.doi.org/10.3390/rs15082077.

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Aimingat non-side-looking airborne radar, we propose a novel unsupervised affinity propagation (AP) clustering radar detection algorithm to suppress clutter and detect targets. The proposed method first uses selected power points as well as space-time adaptive processing (STAP) weight vector, and designs matrix-transformation-based weighted input data, with which the first unsupervised weighted AP clustering is proposed by means of their similarity matrix, responsibility values and availability values. Then, new reconstructed weighted power inputs are designed, and the second weighted AP clust

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Venkanna, Gugulothu, and Dr K. F. Bharati. "Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm." Computer Journal 64, no. 6 (2021): 960–72. http://dx.doi.org/10.1093/comjnl/bxab013.

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Abstract Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering

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Wang, Tao. "Cotraining Algorithm Based on Weighted Principal Component Analysis and Improved Density Peak Clustering." Security and Communication Networks 2022 (September 5, 2022): 1–6. http://dx.doi.org/10.1155/2022/8353697.

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In order to solve the problem of insufficient useful information of unlabeled samples added in the iterative process and the accumulation of classification errors caused by inconsistent labeling of samples by multiple classifiers, a cotraining algorithm based on weighted principal component analysis and improved density peak clustering is proposed. This paper firstly introduces the density peak clustering algorithm and the density peak clustering algorithm based on weighted voting consistency. In terms of experiments, the DPC-VM algorithm will be tested on the real datasets Seed, Haberman, and

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KULIKOV, A. A. "USING GENETIC ALGORITHM IN CLUSTERING PROBLEM FOR WEIGHTED ORIENTED GRAPH." Computational nanotechnology 11, no. 2 (2024): 93–101. http://dx.doi.org/10.33693/2313-223x-2024-11-2-93-101.

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Optimization is a very important concept in any field of business, be it retail, finance, automotive or healthcare. The goal of optimization is to find a point or set of points in the search space by minimizing/maximizing the loss/cost function that gives the optimal solution for the problem at hand. In this case, clustering methods, data mining techniques and clustering optimization algorithms are of particular importance. In this context, metaheuristic algorithms, which include the genetic algorithm, become particularly popular and important. Thus, the aim of the paper is to examine the poss

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Pradnyana, Gede Aditra, and Arif Djunaidy. "High Scalability Document Clustering Algorithm Based On Top-K Weighted Closed Frequent Itemsets." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (2021): 359–68. http://dx.doi.org/10.29207/resti.v5i2.2987.

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Documents clustering based on frequent itemsets can be regarded a new method of documents clustering which is aimed to overcome curse of dimensionality of items produced by documents being clustered. The Maximum Capturing (MC) technique is an algorithm of documents clustering based on frequent itemsets that is capable of producing a better clustering quality in compared to other similar algorithms. However, since the maximum capturing technique employed frequent itemsets, it still suffers from such several weaknesses as the emergence of items redundancy that may still cause curse of dimensiona

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Dai, Yawen, Guanghui Yuan, Zhaoyuan Yang, and Bin Wang. "K-Modes Clustering Algorithm Based on Weighted Overlap Distance and Its Application in Intrusion Detection." Scientific Programming 2021 (May 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/9972589.

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In order to better apply the K-modes algorithm to intrusion detection, this paper overcomes the problems of the existing K-modes algorithm based on rough set theory. Firstly, for the problem of K-modes clustering in the initial class center selection, an initial class center selection algorithm Ini_Weight based on weighted density and weighted overlap distance is proposed. Secondly, based on the Ini_Weight algorithm, a new K-modes clustering algorithm WODKM based on weighted overlap distance is proposed. Thirdly, the WODKM clustering algorithm is applied to intrusion detection to obtain a new

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Yin, Lifeng, Lei Lv, Dingyi Wang, Yingwei Qu, Huayue Chen, and Wu Deng. "Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining." Electronics 12, no. 15 (2023): 3284. http://dx.doi.org/10.3390/electronics12153284.

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This paper proposes a spectral clustering method using k-means and weighted Mahalanobis distance (Referred to as MDLSC) to enhance the degree of correlation between data points and improve the clustering accuracy of Laplacian matrix eigenvectors. First, we used the correlation coefficient as the weight of the Mahalanobis distance to calculate the weighted Mahalanobis distance between any two data points and constructed the weighted Mahalanobis distance matrix of the data set; then, based on the weighted Mahalanobis distance matrix, we used the K-nearest neighborhood (KNN) algorithm construct s

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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 <strong>Objectives:</strong> 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. <strong>Methods:</strong> 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,

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M., Nithya, K. Balasubramaium, and Senthil S. "MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications." Journal of Electrical and Electronic Engineering 12, no. 2 (2024): 36–47. http://dx.doi.org/10.11648/j.jeee.20241202.12.

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Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuiti

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Bhardwaj, Rupali, V. S. Dixit, and Anil Upadhyay. "Load Balancing in Peer-to-Peer System Using Fuzzy C-Means Clustering." International Journal of Fuzzy System Applications 3, no. 1 (2013): 82–93. http://dx.doi.org/10.4018/ijfsa.2013010105.

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Objective of load balancing algorithm is to keep all nodes normally loaded through migration of modules from heavy weighted nodes to light weighted nodes. In addition, load balancing must involve low communication overhead and respond quickly to load imbalance in the system. In previous load balancing algorithms, classification of nodes is done by using threshold value; which is fixed and predefined. In this paper, the authors proposed load balancing algorithm using fuzzy c-means clustering which changed the status of nodes dynamically according to the state of system. The proposed algorithm i

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YU, Qiyue, Weixiao MENG, and Fumiyuki ADACHI. "An Adaptive Weighted Clustering Algorithm for Cooperative Communications." IEICE Transactions on Communications E94-B, no. 12 (2011): 3251–58. http://dx.doi.org/10.1587/transcom.e94.b.3251.

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Caron, Eddy, Ajoy K. Datta, Benjamin Depardon, and Lawrence L. Larmore. "A self-stabilizing -clustering algorithm for weighted graphs." Journal of Parallel and Distributed Computing 70, no. 11 (2010): 1159–73. http://dx.doi.org/10.1016/j.jpdc.2010.06.009.

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Wu, Di, and Jiadong Ren. "Sequence clustering algorithm based on weighted vector identification." International Journal of Machine Learning and Cybernetics 8, no. 3 (2015): 731–38. http://dx.doi.org/10.1007/s13042-015-0381-2.

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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Improved SOSK-Means Automatic Clustering Algorithm with a Three-Part Mutualism Phase and Random Weighted Reflection Coefficient for High-Dimensional Datasets." Applied Sciences 12, no. 24 (2022): 13019. http://dx.doi.org/10.3390/app122413019.

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Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend the capacity of K-means in handling automatic clustering problems. In this study, we proposed an improved version of an existing hybridization of the classical symbiotic organisms search algorithm with the classical K-means algorithm to provide robust and optimum data clustering perfor

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Wen, Wu Di, Zhong Le Liu, and Hua Li. "A Method to Ascertain Parameters of Samples and their Feature Weights in the Weighted Fuzzy Clustering." Applied Mechanics and Materials 300-301 (February 2013): 653–58. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.653.

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The reasonable definitions of samples and their feature weights in weighted fuzzy clustering algorithm based on the thought of normalization and each computational formula are presented. Banding together with computational formulas of samples and their feature weights which derived in weighted FCM, we can get the regions of sample’s weight parameter() and sample feature’s weight parameter(). Then divide the regions into intervals, point out the clustering situations in different intervals and how changing of and affect the clustering result and the choice of feature.Try to explore the relation

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Yu, Xin, Feng Zeng, Deborah Simon Mwakapesa, et al. "DBWGIE-MR: A density-based clustering algorithm by using the weighted grid and information entropy based on MapReduce." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 10781–96. http://dx.doi.org/10.3233/jifs-201792.

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The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clustering algorithm based on density. This algorithm is implemented in three stages: data partitioning, local clustering, and global clustering. For each stage, we propose several strategies to improve the algorithm. In the first stage, based on the spatial distribution of dat

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Zeng, Shaohua, Yalan Wu, Shuai Wang, and Ping He. "Adaptive scale weighted fuzzy C-Means clustering for the segmentation of purple soil color image." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11201–15. http://dx.doi.org/10.3233/jifs-202401.

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The segmentation and extraction of the purple soil region from purple soil color image can effectively avoid the influence of background on recognition of soil types. A scale weighted fuzzy c-means clustering algorithm(SWFCM) is proposed for effective segmentation of purple soil color image. The main work is to establish the maximum difference optimization model with the mean of Gaussian distance between each pixel and each peak of the image histogram, and optimize the clustering number and the initial clustering centers. Then, the compactness of each class is defined to weight the Euclidean d

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HAMID MUHAMMED, HAMED. "USING WEIGHTED FIXED NEURAL NETWORKS FOR UNSUPERVISED FUZZY CLUSTERING." International Journal of Neural Systems 12, no. 06 (2002): 425–34. http://dx.doi.org/10.1142/s0129065702001321.

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A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Network (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces a weighted connected net, of weighted nodes connected by weighted edges, which reflects and preserves the topology of the input data set. The weights of the nodes and the edges in the resulting net are proportional to the local densities of data samples in input space. The connectedness of the net can be changed, and the higher the connectedness

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Wan, Xinhang, Xinwang Liu, Jiyuan Liu, et al. "Auto-Weighted Multi-View Clustering for Large-Scale Data." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10078–86. http://dx.doi.org/10.1609/aaai.v37i8.26201.

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Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparamet

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Budimirovic, Nebojsa, and Nebojsa Bacanin. "Novel Algorithms for Graph Clustering Applied to Human Activities." Mathematics 9, no. 10 (2021): 1089. http://dx.doi.org/10.3390/math9101089.

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In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented. Additionally, a new measure of evaluation of clustering results is given—the accuracy of formed clusters (T). For the purpose of clustering human activities, the procedure of forming string sequences are presented. String symbols are gained by modeling spatiotemporal signals obtained from inertial measurement units. String sequences provided a

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MUHAMMED, HAMED HAMID. "UNSUPERVISED FUZZY CLUSTERING USING WEIGHTED INCREMENTAL NEURAL NETWORKS." International Journal of Neural Systems 14, no. 06 (2004): 355–71. http://dx.doi.org/10.1142/s0129065704002121.

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A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given inp

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WU, XIAODONG, DANNY Z. CHEN, JAMES J. MASON, and STEVEN R. SCHMID. "EFFICIENT APPROXIMATION ALGORITHMS FOR PAIRWISE DATA CLUSTERING AND APPLICATIONS." International Journal of Computational Geometry & Applications 14, no. 01n02 (2004): 85–104. http://dx.doi.org/10.1142/s0218195904001378.

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Data clustering is an important theoretical topic and a sharp tool for various applications. It is a task frequently arising in geometric computing. The main objective of data clustering is to partition a given data set into clusters such that the data items within the same cluster are "more" similar to each other with respect to certain measures. In this paper, we study the pairwise data clustering problem with pairwise similarity/dissimilarity measures that need not satisfy the triangle inequality. By using a criterion, called the minimum normalized cut, we model the general pairwise data cl

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Correa, Blanca Alicia, Laura Ospina, and Roberto Carlos Hincapié. "Survey of clustering techniques for mobile ad hoc networks." Revista Facultad de Ingeniería Universidad de Antioquia, no. 41 (March 31, 2014): 145–61. http://dx.doi.org/10.17533/udea.redin.19022.

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Clustering methods allow fast connection and also better routing and topology management of mobile ad hoc networks (MANET). In this paper a survey of clustering techniques for MANET is presented and some preliminary concepts for designing clustering algorithms are introduced. These concepts relate to network topology, routing schemes, graph partitioning and mobility algorithms. In addition, some of the most popular clustering techniques, such as Lowest-ID heuristic, Highest degree heuristic, DMAC (distributed mobility-adaptive clustering), and WCA (weighted clustering algorithm), among other t

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Liu, Yongli, Zhonghui Wang, and Hao Chao. "A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization." Journal of Electrical and Computer Engineering 2021 (September 6, 2021): 1–13. http://dx.doi.org/10.1155/2021/7387153.

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Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implemen

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Zhang, Wenyuan, Xijuan Guo, Tianyu Huang, Jiale Liu, and Jun Chen. "Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm." Symmetry 11, no. 6 (2019): 753. http://dx.doi.org/10.3390/sym11060753.

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The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clust

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Hua, Lei, Jing Xue, Kaijian Xia, Leyuan Zhou, Pengjiang Qian, and Yizhang Jiang. "An Automatic Magnetic Resonance Brain Image Segmentation Method Using a Multitask Weighted Fuzzy Clustering Algorithm." Journal of Medical Imaging and Health Informatics 11, no. 8 (2021): 2241–47. http://dx.doi.org/10.1166/jmihi.2021.3705.

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In clinical assisted diagnosis, it is an important way to obtain information with the help of medical images. Qualitative and quantitative analysis of brain tissue has become a research hotspot for brain diseases. Therefore, image segmentation technology is an indispensable link in medical image analysis. Due to the defects such as ambiguity, complexity, gray-scale unevenness, partial volume effect in magnetic resonance brain images, it is essential to improve the segmentation performance of classical algorithms in medical images. In this paper, multitasking and weighted fuzzy clustering algor

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Wang, Zhen Bo, and Bao Zhi Qiu. "Fuzzy C-Means Clustering Algorithm Based on Coefficient of Variation." Advanced Materials Research 998-999 (July 2014): 873–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.873.

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To reduce the impact of irrelevant attributes on clustering results, and improve the importance of relevant attributes to clustering, this paper proposes fuzzy C-means clustering algorithm based on coefficient of variation (CV-FCM). In the algorithm, coefficient of variation is used to weigh attributes so as to assign different weights to each attribute in the data set, and the magnitude of weight is used to express the importance of different attributes to clusters. In addition, for the characteristic of fuzzy C-means clustering algorithm that it is susceptible to initial cluster center value

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Hussain, Ishtiaq, Yessica Nataliani, Mehboob Ali, et al. "Weighted Multiview K-Means Clustering with L2 Regularization." Symmetry 16, no. 12 (2024): 1646. https://doi.org/10.3390/sym16121646.

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In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples. Despite being sourced from diverse settings and domains, these data serve the common purpose of describing the same samples, establishing a significant interrelation among them. Thus, there arises a necessity for the development of multiview clustering methodologies capable of leveraging the wealth

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