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Journal articles on the topic 'HYBRID CLUSTERING'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Osei-Bryson, Kweku-Muata, and Tasha R. Inniss. "A hybrid clustering algorithm." Computers & Operations Research 34, no. 11 (2007): 3255–69. http://dx.doi.org/10.1016/j.cor.2005.12.004.

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Swati Gupta. "A Novel Hybrid Model Based on Hierarchical and Density Clustering Approaches." Panamerican Mathematical Journal 35, no. 2s (2024): 329–39. https://doi.org/10.52783/pmj.v35.i2s.2575.

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Prediction is a developing domain that requires further advancement. In practical situations, one frequently encounters unsupervised datasets requiring analysis. Clustering techniques can efficiently resolve this difficulty. The current study designs the hybrid hierarchical density model to enhance clustering efficiency through a combination of hierarchical clustering and DBSCAN. Hierarchical clustering's initial partitioning quality, which lays a strong foundation for DBSCAN's refinement, is a key influencing factor for these enhancements in this model. DBSCAN's ability to adjust to local den

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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Boosting k-means clustering with symbiotic organisms search for automatic clustering problems." PLOS ONE 17, no. 8 (2022): e0272861. http://dx.doi.org/10.1371/journal.pone.0272861.

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Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performa

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Tusell-Rey, Claudia C., Yenny Villuendas-Rey, Viridiana Salinas-García, Oscar Camacho-Nieto, and Cornelio Yáñez-Márquez. "Instance Selection for Hybrid and Incomplete Data based on Clustering." International Journal of Combinatorial Optimization Problems and Informatics 16, no. 3 (2025): 405–19. https://doi.org/10.61467/2007.1558.2025.v16i3.845.

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This paper presents the HICCS algorithm, a novel clustering approach that handles mixed and incomplete data. HICCS improves clustering by using compact sets as initial clusters, employing holotypes to measure intergroup dissimilarity, and merging clusters based on similarity in an order-independent manner. Additionally, it incorporates a user-defined similarity function, making it adaptable to various real-world domains. Furthermore, we introduce the IS-HICCS algorithm for instance selection, which reduces the instance set without compromising classifier accuracy, highlighting clustering's pot

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Augusteijn, M. F., and U. J. Steck. "Supervised adaptive clustering: A hybrid neural network clustering algorithm." Neural Computing & Applications 7, no. 1 (1998): 78–89. http://dx.doi.org/10.1007/bf01413712.

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Guo, Limin, Weijia Qin, Zhi Cai, and Xing Su. "Hybrid Clustering Algorithm Based on Improved Density Peak Clustering." Applied Sciences 14, no. 2 (2024): 715. http://dx.doi.org/10.3390/app14020715.

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In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several

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Yu, Zhiwen, Le Li, Yunjun Gao, et al. "Hybrid clustering solution selection strategy." Pattern Recognition 47, no. 10 (2014): 3362–75. http://dx.doi.org/10.1016/j.patcog.2014.04.005.

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Amiri, Saeid, Bertrand S. Clarke, Jennifer L. Clarke, and Hoyt Koepke. "A General Hybrid Clustering Technique." Journal of Computational and Graphical Statistics 28, no. 3 (2019): 540–51. http://dx.doi.org/10.1080/10618600.2018.1546593.

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9

Javed, Ali, and Byung Suk Lee. "Hybrid semantic clustering of hashtags." Online Social Networks and Media 5 (March 2018): 23–36. http://dx.doi.org/10.1016/j.osnem.2017.10.004.

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10

Rakhi. "Hybrid Technique on Image Clustering." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 354–58. https://doi.org/10.35940/ijeat.F1483.089620.

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Face recognition using FLD for extracting high dimensional images is introduced in this paper. The main purpose is to work on removing bugs and noise from the images and extract the facial expression applied on face descriptor. FLD is selected for increasing the discrimination information [17]. The main points of this paper give the brief knowledge about the face recognition and face clustering. Its shows how biometric terms help the local and global features for extracting information from database. Finding better solutions to deal with noise in face recognition is a challenging task [18]. We

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Chen, Yan, and Qin Zhou Niu. "Hybrid Clustering Algorithm Based on KNN and MCL." Applied Mechanics and Materials 610 (August 2014): 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.610.302.

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MCL is a graph clustering algorithm. With the characteristics of the MCL computational process, MCL is prone to producing small clustering and separating edge nodes from the group. A hybrid clustering based on MCL combined with KNN algorithm is proposed. Hybrid algorithm improves the quality of clustering by reclassification of elements in small clustering by using KNN classification characteristics and Clustering tables required by MCL clustering. Experiment proves the improved algorithm can enhance the quality of clustering.

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LIU, YONGGUO, XIAORONG PU, YIDONG SHEN, ZHANG YI, and XIAOFENG LIAO. "CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM." International Journal on Artificial Intelligence Tools 16, no. 06 (2007): 919–34. http://dx.doi.org/10.1142/s021821300700362x.

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In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demo

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P. Saveetha, P. Saveetha, Y. Harold Robinson P. Saveetha, Vimal Shanmuganathan Y. Harold Robinson, Seifedine Kadry Vimal Shanmuganathan, and Yunyoung Nam Seifedine Kadry. "Hybrid Energy-based Secured Clustering technique for Wireless Sensor Networks." 網際網路技術學刊 23, no. 1 (2022): 021–31. http://dx.doi.org/10.53106/160792642022012301003.

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<p>The performance of the Wireless sensor networks (WSNs) identified as the efficient energy utilization and enhanced network lifetime. The multi-hop path routing techniques in WSNs have been observed that the applications with the data transmission within the cluster head and the base station, so that the intra-cluster transmission has been involved for improving the quality of service. This paper proposes a novel Hybrid Energy-based Secured Clustering (HESC) technique for providing the data transmission technique for WSNs to produce the solution for the energy and security problem for

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Zhang, Ren-Long, and Xiao-Hong Liu. "A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy." Applied Sciences 13, no. 3 (2023): 1246. http://dx.doi.org/10.3390/app13031246.

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With the increase in the number of high-dimensional data, the characteristic phenomenon of unbalanced distribution is increasingly presented in various big data applications. At the same time, most of the existing clustering and feature selection algorithms are based on maximizing the clustering accuracy. In addition, the hybrid approach can effectively solve the clustering problem of unbalanced data. Aiming at the shortcomings of the unbalanced data clustering algorithm, a hybrid high-dimensional multi-objective PSO clustering algorithm is proposed based on the cloud model and entropy (HHCE-M

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Villuendas-Rey, Yenny, Claudia C. Tusell-rey, Oscar Camacho-Nieto, and Viridiana Salinas-García. "Bioinspired Hybrid and Incomplete Data Clustering." International Journal of Combinatorial Optimization Problems and Informatics 15, no. 4 (2024): 85–100. http://dx.doi.org/10.61467/2007.1558.2024.v15i4.501.

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Enhancing the results of data clustering for hybrid (numeric and categorical) and missing data is paramount for robust pattern recognition and decision-making in various fields. Traditional clustering algorithms often need help with heterogeneous data types and incomplete information, leading to suboptimal groupings and potentially biased insights. By addressing these challenges, advanced techniques, such as bioinspired algorithms, can provide more accurate and comprehensive clustering. We show the usefulness of using internal validity indices in obtaining high-quality clusters for hybrid and

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Mosorov, Volodymyr, Taras Panskyi, and Sebastian Biedron. "TESTING FOR REVEALING OF DATA STRUCTURE BASED ON THE HYBRID APPROACH." Informatics Control Measurement in Economy and Environment Protection 7, no. 2 (2017): 119–22. http://dx.doi.org/10.5604/01.3001.0010.4853.

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In this paper testing for revealing data structure based on a hybrid approach has been presented. The hybrid approach used during the testing suggests defining a pre-clustering hypothesis, defining a pre-clustering statistic and assuming the homogeneity of the data under pre-defined hypothesis, applying the same clustering procedure for a data set of interest, and comparing results obtained under the pre-clustering statistic with the results from the data set of interest. The pros and cons of the hybrid approach have been also considered.

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Zhang, Jian, and Zongheng Ma. "Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)." Computational Intelligence and Neuroscience 2020 (March 18, 2020): 1–12. http://dx.doi.org/10.1155/2020/1386839.

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Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms

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18

Al Rivan, Muhammad Ezar, Giovani Prakasa Gandi, and Fendy Novianto Lukman. "Perbandingan Hybrid Genetic K-Means++ dan Hybrid Genetic K-Medoid untuk Klasterisasi Dataset EEG Eyestate." PETIR 14, no. 1 (2020): 103–13. http://dx.doi.org/10.33322/petir.v14i1.953.

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K-Means++ and K-Medoids are data clustering methods. The data cluster speed is determined by the iteration value, the lower the iteration value, the faster the data clustering is done. Data clustering performance can be optimized to get more optimal clustering results. One algorithm that can optimize cluster speed is Genetic Algorithm (GA). The dataset used in the study is a dataset of EEG Eyestate. The optimization results before hybrid GA on K-Means++ are the iteration average values is 11.6 to 5,15, and in K-Medoid are the iteration average values decreased from 5.9 to 5.2. Based on the com

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19

Y., A. Joarder, Mustafizur Rahman Kh., and Ullah Ahsan. "A Hybrid Partitioning Algorithm for Robust Big Data Clustering and Analysis." Journal of Network Security and Data Mining 2, no. 3 (2019): 1–16. https://doi.org/10.5281/zenodo.3465227.

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<em>Clustering algorithms try to get groups or clusters of data points that belong together. The main aims of this research are: to improve the K-MEANS clustering quality by removing empty clustering and inefficient data clustering issues using the hybrid partitioning algorithm and to do comparison of advanced experimental results between K-MEANS and the proposed hybrid partitioning algorithm respectively. This research gives surety of achieving high quality clustering that is all in one solution for the foremost well-known problems in data mining. Though, K-MEANS converges fairly quickly, ach

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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.

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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor

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21

Basu, Sumit, Danyel Fisher, Steven Drucker, and Hao Lu. "Assisting Users with Clustering Tasks by Combining Metric Learning and Classification." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 394–400. http://dx.doi.org/10.1609/aaai.v24i1.7694.

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Interactive clustering refers to situations in which a human labeler is willing to assist a learning algorithm in automatically clustering items. We present a related but somewhat different task, assisted clustering, in which a user creates explicit groups of items from a large set and wants suggestions on what items to add to each group. While the traditional approach to interactive clustering has been to use metric learning to induce a distance metric, our situation seems equally amenable to classification. Using clusterings of documents from human subjects, we found that one or the other me

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22

Ma, Ruizhe, Xiaoping Zhu, and Li Yan. "A Hybrid Approach for Clustering Uncertain Time Series." Journal of Computing and Information Technology 28, no. 4 (2021): 255–67. http://dx.doi.org/10.20532/cit.2020.1004802.

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Information uncertainty extensively exists in the real-world applications, and uncertain data process and analysis have been a crucial issue in the area of data and knowledge engineering. In this paper, we concentrate on uncertain time series data clustering, in which the uncertain values at time points are represented by probability density function. We propose a hybrid clustering approach for uncertain time series. Our clustering approach first partitions the uncertain time series data into a set of micro-clusters and then merges the micro-clusters following the idea of hierarchical clusteri

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Chen, Pei-Yin, and Jih-Jeng Huang. "A Hybrid Autoencoder Network for Unsupervised Image Clustering." Algorithms 12, no. 6 (2019): 122. http://dx.doi.org/10.3390/a12060122.

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Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great su

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Yang, Wenlu, Yinghui Zhang, Hongjun Wang, Ping Deng, and Tianrui Li. "Hybrid genetic model for clustering ensemble." Knowledge-Based Systems 231 (November 2021): 107457. http://dx.doi.org/10.1016/j.knosys.2021.107457.

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Sharma, Saurabh, and Vishal Gupta. "Hybrid Approach for Punjabi Text Clustering." International Journal of Computer Applications 52, no. 1 (2012): 32–36. http://dx.doi.org/10.5120/8167-1407.

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Kim, Sung-Soo, Jun-Young Baek, and Beom-Soo Kang. "Hybrid Simulated Annealing for Data Clustering." Journal of Society of Korea Industrial and Systems Engineering 40, no. 2 (2017): 92–98. http://dx.doi.org/10.11627/jkise.2017.40.2.092.

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Tinos, Renato, Liang Zhao, Francisco Chicano, and Darrell Whitley. "NK Hybrid Genetic Algorithm for Clustering." IEEE Transactions on Evolutionary Computation 22, no. 5 (2018): 748–61. http://dx.doi.org/10.1109/tevc.2018.2828643.

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Che, Z. H. "A hybrid algorithm for fuzzy clustering." European J. of Industrial Engineering 6, no. 1 (2012): 50. http://dx.doi.org/10.1504/ejie.2012.044810.

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Nguyen, Cao D., and Krzysztof J. Cios. "GAKREM: A novel hybrid clustering algorithm." Information Sciences 178, no. 22 (2008): 4205–27. http://dx.doi.org/10.1016/j.ins.2008.07.016.

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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets." Applied Sciences 12, no. 23 (2022): 12275. http://dx.doi.org/10.3390/app122312275.

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Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to th

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Gao, Zhiqiang, Yixiao Sun, Xiaolong Cui, Yutao Wang, Yanyu Duan, and Xu An Wang. "Privacy-Preserving Hybrid K-Means." International Journal of Data Warehousing and Mining 14, no. 2 (2018): 1–17. http://dx.doi.org/10.4018/ijdwm.2018040101.

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This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in re

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Golpîra, Hêriş. "A Hybrid Clustering Method Using Balanced Scorecard and Data Envelopment Analysis." International Journal Of Innovation And Economic Development 1, no. 7 (2015): 15–25. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.17.2002.

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This paper introduces a new hybrid clustering method using Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) methods. DEA cannot identify its’ input and output itself, and it is a major weakness of the DEA. In the proposed method, this gap is resolved by integrating DEA with BSC. Some decision-making units (DMUs) needed in DEA method, in compliance with some inputs and outputs is the major drawback of this integration. To deal with this disadvantage, the proposed method selects the most important strategic factors, attained from the BSC method. These data considered to be the input

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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

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Satish, Gajawada, and Toshniwal Durga. "Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution." International Journal of Software Engineering & Applications (IJSEA) 3, no. 4 (2020): 77–85. https://doi.org/10.5281/zenodo.4033890.

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Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have been solved by using DE based clustering methods but these methods may fail to find clusters hidden in subspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed in literature to find subspace clusters that are present in subspaces of dataset. In this paper we propose VINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE optimizes a hybrid cluster validation index. Subspace Clustering Quality Estimate index (SCQE index) i

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Pourahmad, Saeedeh, Atefeh Basirat, Amir Rahimi, and Marziyeh Doostfatemeh. "Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study." Computational and Mathematical Methods in Medicine 2020 (August 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/7636857.

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Random selection of initial centroids (centers) for clusters is a fundamental defect in K-means clustering algorithm as the algorithm’s performance depends on initial centroids and may end up in local optimizations. Various hybrid methods have been introduced to resolve this defect in K-means clustering algorithm. As regards, there are no comparative studies comparing these methods in various aspects, the present paper compared three hybrid methods with K-means clustering algorithm using concepts of genetic algorithm, minimum spanning tree, and hierarchical clustering method. Although these th

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Maurya, Roshankumar Ramashish, and Anand Khandare. "Enhance Clustering Algorithm Using Optimization." International Journal of Research in Engineering, Science and Management 3, no. 9 (2020): 136–42. http://dx.doi.org/10.47607/ijresm.2020.313.

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Unsupervised learning can reveal the structure of datasets without being concerned with any labels, K-means clustering is one such method. Traditionally the initial clusters have been selected randomly, with the idea that the algorithm will generate better clusters. However, studies have shown there are methods to improve this initial clustering as well as the K-means process. This paper examines these results on different types of datasets to study if these results hold for all types of data. Another method that is used for unsupervised clustering is the algorithm based on Particle Swarm Opti

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Yang, Yong Sheng, Gang Li, Yong Sheng Zhu, and You Yun Zhang. "Hybrid Genetic Clustering by Using FCM and Geodesic Distance for Complex Distributed Data." Applied Mechanics and Materials 263-266 (December 2012): 2597–601. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2597.

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To efficiently find hidden clusters in datasets with complex distributed data，inspired by complementary strategies, a hybrid genetic clustering algorithm was developed, which is on the basis of the geodesic distance metric, and combined with the Fuzzy C-Means clustering (FCM) algorithm. First, instead of using Euclidean distance，the new approach employs geodesic distance based dissimilarity metric during all fitness evaluation. And then, with the help of FCM clustering, some sub-clusters with spherical distribution are partitioned effectively. Next, a genetic algorithm based clustering using g

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Chen, Xin, Yongquan Zhou, and Qifang Luo. "A Hybrid Monkey Search Algorithm for Clustering Analysis." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/938239.

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Clustering is a popular data analysis and data mining technique. Thek-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of thek-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

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Kanungo, D. P., Janmenjoy Nayak, Bighnaraj Naik, and H. S. Behera. "Hybrid Clustering using Elitist Teaching Learning-Based Optimization." International Journal of Rough Sets and Data Analysis 3, no. 1 (2016): 1–19. http://dx.doi.org/10.4018/ijrsda.2016010101.

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Data clustering is a key field of research in the pattern recognition arena. Although clustering is an unsupervised learning technique, numerous efforts have been made in both hard and soft clustering. In hard clustering, K-means is the most popular method and is being used in diversified application areas. In this paper, an effort has been made with a recently developed population based metaheuristic called Elitist based teaching learning based optimization (ETLBO) for data clustering. The ETLBO has been hybridized with K-means algorithm (ETLBO-K-means) to get the optimal cluster centers and

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Takale, Dattatray G., Parikshit N. Mahalle, Omkaresh Kulkarni, et al. "Hybrid algorithm for fault node recovery and energy efficiency in wireless sensor networks." Journal of Information and Optimization Sciences 45, no. 8 (2024): 2347–67. https://doi.org/10.47974/jios-1831.

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The Wireless Sensor Networks (WSNs) are designed for the monitoring of remote areas in various places with a variety of different applications. The main challenges with the WSN are energy efficiency and fault recovery. In order to optimize the network lifetime of the WSN, fault node recovery and energy efficient clustering are required in order to efficiently utilize the energy supply device of battery-powered sensors. The purpose of this paper is to develop a hybrid algorithm that combines the K-means clustering technique with fault node recovery in the WSN in order to reduce energy consumpti

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Shi, Maolin, Zihao Wang, and Lizhang Xu. "A fuzzy clustering algorithm based on hybrid surrogate model." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1963–76. http://dx.doi.org/10.3233/jifs-211340.

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Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c-means algorithm, in which the differences between the clusters are evaluated by

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Prasetyadi, Abdurrakhman, Budi Nugroho, and Adrin Tohari. "A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering." Journal of Information and Communication Technology 21, No.2 (2022): 175–200. http://dx.doi.org/10.32890/jict2022.21.2.2.

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Cluster methods such as k-means have been widely used to group areas with a relatively equal number of disasters to determine areas prone to natural disasters. Nevertheless, it is dificult to obtain a homogeneous clustering result of the k-means method because this method is sensitive to a random selection of the centers of the cluster. This paper presents the result of a study that aimed to apply a proposed hybrid approach of the combined k-means algorithm and hierarchy to the clustering process of anticipation level datasets of natural disaster mitigation in Indonesia. This study also added

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Alfira, Astrid, Fariani Hermin, and Eti Dwi Wiraningsih. "Analisis Hybrid Mutual Clustering menggunakan Jarak Square Euclidean." JMT : Jurnal Matematika dan Terapan 1, no. 1 (2017): 15–21. http://dx.doi.org/10.21009/jmt.1.1.3.

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ABSTRAK Analisis kelompok berguna untuk mengelompokkan objek berdasarkan ukuran kemiripan, dimana konsep dasar dari analisis kelompok adalah pengukuran jarak dan kesamaan. Pengelompokan objek di dalam analisis kelompok dapat dilakukan dengan metode bottom-up, top-down, dan Hybrid Mutual Clustering. Pengelompokan objek dengan bottom-up menggunakan metode pengelompokan yang dimulai dari kelompok kecil menjadi kelompok yang lebih besar, pengelompokan objek dengan top-down menggunakan metode sebaliknya yaitu pengelompokan dengan memecah kelompok besar menjadi kelompok yang lebih kecil. Metod

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Rashid, Omar Fitian, and Mazin S. Al-Hakeem. "Hybrid Intrusion Detection System based on DNA Encoding, Teiresias Algorithm and Clustering Method." Webology 19, no. 1 (2022): 508–20. http://dx.doi.org/10.14704/web/v19i1/web19036.

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Until recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In

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Inbarani H, Hannah, Selva Kumar S, Ahmad Taher Azar, and Aboul Ella Hassanien. "Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System." International Journal of Rough Sets and Data Analysis 2, no. 1 (2015): 22–37. http://dx.doi.org/10.4018/ijrsda.2015010102.

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Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social

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Inbarani H, Hannah, and Selva Kumar S. "Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System." International Journal of Rough Sets and Data Analysis 2, no. 1 (2015): 70–87. http://dx.doi.org/10.4018/ijrsda.2015010105.

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Social tagging is one of the vital attributes of WEB2.0. The challenge of Web 2.0 is a gigantic measure of information created over a brief time. Tags are broadly used to interpret and arrange the web 2.0 assets. Tag clustering is the procedure of grouping the comparable tags into clusters. The tag clustering is extremely valuable for researching and organizing the web2. 0 resources furthermore critical for the achievement of Social Bookmarking frameworks. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Firefly (TRS-Firefly-K-Means) clustering algorithm for clustering ta

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Hardika, Khusnuliawati, and Riskiana Putri Dhian. "Hybrid clustering based on multi-criteria segmentation for higher education marketing." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 5 (2021): 1498–506. https://doi.org/10.12928/telkomnika.v19i5.18965.

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Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful in

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Lins Galdino, Sérgio Mário, and Jornandes Dias da Silva. "Hybrid Clustering: Combining K-Means and Interval valued data-type Hierarchical Clustering." Acta Polytechnica Hungarica 21, no. 9 (2024): 175–86. http://dx.doi.org/10.12700/aph.21.9.2024.9.12.

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Zuhanda, Muhammad Khahfi, Kelvin Leonardi Kohsasih, Pieter Octaviandy, et al. "Hybrid Deep Fixed K-Means (HDF-KMeans)." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 102–11. https://doi.org/10.52088/ijesty.v5i3.913.

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K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: De

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Susmi, S. Jacophine, H. Khanna Nehemiah, A. Kannan, and G. Saranya. "Hybrid Algorithm for Clustering Gene Expression Data." Research Journal of Applied Sciences, Engineering and Technology 11, no. 7 (2015): 692–700. http://dx.doi.org/10.19026/rjaset.11.2032.

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