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Relevant bibliographies by topics / HYBRID CLUSTERING

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Journal articles

Academic literature on the topic 'HYBRID CLUSTERING'

Author: Grafiati

Published: 11 September 2023

Last updated: 12 June 2025

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Journal articles on the topic "HYBRID CLUSTERING"

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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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 performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.

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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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4

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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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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7

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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8

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 cluster based data transmission. The proposed technique involves the formation of clusters to perform the organization of sensor nodes with the multi-hop data transmission technique for finding the specific node to deliver the data packets to the cluster head node and the secured transmission technique is used to provide the privacy of the sensor nodes through the cluster. The residual energy of the sensor nodes is another parameter to select the forwarding node. The simulation results can show the efficiency of this proposed technique in spite of lifetime within the huge amount data packets. The security of this proposed technique is measured and increases the performance of the proposed technique.</p> <p>&nbsp;</p>

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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 demonstrated.

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10

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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