Статті в журналах: "HYBRID CLUSTERING"

1

Osei-Bryson, Kweku-Muata, and Tasha R. Inniss. "A hybrid clustering algorithm." Computers & Operations Research 34, no. 11 (November 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 (August 11, 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 (March 1998): 78–89. http://dx.doi.org/10.1007/bf01413712.

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Yu, Zhiwen, Le Li, Yunjun Gao, Jane You, Jiming Liu, Hau-San Wong, and Guoqiang Han. "Hybrid clustering solution selection strategy." Pattern Recognition 47, no. 10 (October 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 (March 18, 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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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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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 (January 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> </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 (December 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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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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11

Sharma, Saurabh, and Vishal Gupta. "Hybrid Approach for Punjabi Text Clustering." International Journal of Computer Applications 52, no. 1 (August 30, 2012): 32–36. http://dx.doi.org/10.5120/8167-1407.

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12

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 (June 30, 2017): 92–98. http://dx.doi.org/10.11627/jkise.2017.40.2.092.

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13

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

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14

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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15

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

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16

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 data for the DEA method to calculate relative closeness (RC) of each DMU to the ideal one. Plotting the screen diagram regarding RC index leads us to the final clustering method. Finally, computational results show the applicability and usefulness of the method.

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17

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 (October 2, 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 comparison of GA K-Means++ and GA K-Medoids iterations, it can be concluded that GA - K-Means++ better

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18

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 (January 17, 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-MOPSO). Furthermore, the feasibility of the hybrid PSO is verified by the simulation of the multi-objective test function. The results not only broaden the new theory and method of clustering algorithm for unbalanced data, but also verify the accuracy and feasibility of the hybrid PSO. Furthermore, the clustering analysis method based on information entropy is a new method. As a result, the research results have both important scientific value and good practical significance.

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19

Ma, Ruizhe, Xiaoping Zhu, and Li Yan. "A Hybrid Approach for Clustering Uncertain Time Series." Journal of Computing and Information Technology 28, no. 4 (October 21, 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 clustering. We evaluate our approach with experiments. The experimental results show that, compared with the traditional UK-means clustering algorithm, the Adjusted Rand Index (ARI) of our clustering results have an obviously higher accuracy. In addition, the time efficiency of our clustering approach is significantly improved.

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20

Chen, Pei-Yin, and Jih-Jeng Huang. "A Hybrid Autoencoder Network for Unsupervised Image Clustering." Algorithms 12, no. 6 (June 15, 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 success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment.

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21

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 (March 4, 2022): 3227–40. http://dx.doi.org/10.3233/jifs-211034.

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

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22

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 (June 30, 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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23

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 require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.

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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 (July 3, 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 method proved to be superior for a given cluster, but not uniformly so. We thus developed a hybrid mechanism for combining the metric learner and the classifier. We present results from a large number of trials based on human clusterings, in which we show that our combination scheme matches and often exceeds the performance of a method which exclusively uses either type of learner.

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25

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 (April 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 resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.

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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 (June 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 nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.

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27

Maurya, Roshankumar Ramashish, and Anand Khandare. "Enhance Clustering Algorithm Using Optimization." International Journal of Research in Engineering, Science and Management 3, no. 9 (September 28, 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 Optimization. For the second part this paper studies the classic K-means based algorithm and a Hybrid K-means algorithm which uses PSO to improve the results from K-means. The hybrid K-means algorithms are compared to the standard K-means clustering on two benchmark classification problems. In this project we used Kaggle dataset to with different size (small, large and medium) for comparison PSO, k-means and k-means hybrid.

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28

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 (November 30, 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 the clustering performance of the resultant hybrid algorithms in terms of computational cost. Reducing the computation time required in the K-means phase of the hybrid algorithm for the automatic clustering of high-dimensional datasets will inevitably reduce the algorithm’s complexity. In this paper, a preprocessing phase is introduced into the K-means phase of an improved firefly-based K-means hybrid algorithm using the concept of the central limit theorem to partition the high-dimensional dataset into subgroups of randomly formed subsets on which the K-means algorithm is applied to obtain representative cluster centers for the final clustering procedure. The enhanced firefly algorithm (FA) is hybridized with the CLT-based K-means algorithm to automatically determine the optimum number of cluster centroids and generate corresponding optimum initial cluster centroids for the K-means algorithm to achieve optimal global convergence. Twenty high-dimensional datasets from the UCI machine learning repository are used to investigate the performance of the proposed algorithm. The empirical results indicate that the hybrid FA-K-means clustering method demonstrates statistically significant superiority in the employed performance measures and reducing computation time cost for clustering high-dimensional dataset problems, compared to other advanced hybrid search variants.

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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 geodesic distance metric, named GCGD, is adopted to cluster the clustering centers obtained from FCM clustering. Finally, the final results are acquired based on above two clustering results. Experimental results on eight benchmark datasets clustering questions show the effectiveness of the algorithm as a clustering technique. Compared with conventional GCGD, the hybrid clustering can decrease the computational time obviously, while retaining high clustering correct ratio.

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30

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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31

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 (January 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 effective fitness values. The performance of the proposed method has been compared with other techniques by considering standard benchmark real life datasets as well as some synthetic datasets. Simulation and comparison results demonstrate the effectiveness and efficiency of the proposed method.

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32

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 (January 20, 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 this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15 database by dividing the record's attributes into four groups, including State, Protocol, Service, and the rest of the features is Digits. Four DNA characters were used to represent each protocol attribute values. While two DNA characters are used to represent State, Service and Digits attributes values. Then, the clustering method is applied to classify the records into two clusters, either attack or normal. The current experiment results showed that the proposed system has achieved a good detection rate and accuracy results equal to 81.22% and 82.05% respectively. Also, the system achieved fast encoding and clustering time that equal 0.385 seconds and 0.00325 seconds respectively for each record.

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D., Saravanakumar. "Improving Microarray Data Classification Using Optimized Clustering-Based Hybrid Gene Selection Algorithm." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 486–95. http://dx.doi.org/10.5373/jardcs/v12sp3/20201283.

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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 (February 2, 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 the regression relationship instead of Euclidean distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships.

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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 three hybrid methods have received more attention in previous researches, fewer studies have compared their results. Hence, seven quantitative datasets with different characteristics in terms of sample size, number of features, and number of different classes are utilized in present study. Eleven indices of external and internal evaluating index were also considered for comparing the methods. Data indicated that the hybrid methods resulted in higher convergence rate in obtaining the final solution than the ordinary K-means method. Furthermore, the hybrid method with hierarchical clustering algorithm converges to the optimal solution with less iteration than the other two hybrid methods. However, hybrid methods with minimal spanning trees and genetic algorithms may not always or often be more effective than the ordinary K-means method. Therefore, despite the computational complexity, these three hybrid methods have not led to much improvement in the K-means method. However, a simulation study is required to compare the methods and complete the conclusion.

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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 (November 5, 2015): 692–700. http://dx.doi.org/10.19026/rjaset.11.2032.

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Mashtalir, Sergey V., Mikhail І. Stolbovoi, and Sergey V. Yakovlev. "Hybrid Approach to Clustering Various Lengths Video." Journal of Automation and Information Sciences 51, no. 3 (2019): 26–35. http://dx.doi.org/10.1615/jautomatinfscien.v51.i3.30.

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38

Lin Li. "New Hybrid Clustering Algorithm for Micro Blog." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 23 (December 31, 2012): 788–95. http://dx.doi.org/10.4156/aiss.vol4.issue23.97.

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39

Prathipati, R. Kumaar, V. Harsha Shastri, Madhavi Kolukuluri, Radha Dharavathu, Donthireddy Sudheer Reddy, and B. N. Siva Rama Krishna. "Hybrid Clustering Approach for Time Series Data." Biomedicine and Chemical Sciences 1, no. 4 (October 1, 2022): 207–14. http://dx.doi.org/10.48112/bcs.v1i4.84.

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The clustering of data series was already demonstrated to provide helpful information in several fields. Initial data for the period is divided into sub-clusters Recorded in the data resemblance. The grouping of data series takes 3 categories, based on which users operate in frequencies or programming interfaces on original data explicitly or implicitly with the characteristics derived from physical information or through a framework based on raw material. The bases of series data grouping are provided. The conditions for the evaluation of the outcomes of grouping are multi-purpose time constant frequently employed in dataset grouping research. A clustering method splits data into different groups so that the resemblance between organisations is better. K-means++ offers an excellent convergence rate compared to other methods. To distinguish the correlation between items the maximum distance is employed. Distance measure metrics are frequently utilized with most methods by many academics. Genetic algorithm for the resolution of cluster issues is worldwide optimization technologies in recent times. The much more prevalent partitioning strategies of large volumes of data are K-Median & K-Median methods. This analysis is focusing on the multiple distance measures, such as Euclidean, Public Square and Shebyshev, hybrid K-means++ and PSO clubs techniques. Comparison to orgorganization-basedthods reveals an excellent classification result compared to the other methods with the K++ PSO method utilizing the Chebyshev distance measure.

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40

González-Almagro, Germán, Juan Luis Suárez, Julián Luengo, José-Ramón Cano, and Salvador García. "3SHACC: Three stages hybrid agglomerative constrained clustering." Neurocomputing 490 (June 2022): 441–61. http://dx.doi.org/10.1016/j.neucom.2021.12.018.

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41

Zhang, Hong. "Graph Based Hybrid Clustering With Unbounded Regions." Journal of Computer Science Applications and Information Technology 2, no. 2 (2017): 1–5. http://dx.doi.org/10.15226/2474-9257/2/2/00113.

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42

Jeet, Kawal, and Renu Dhir. "Software Module Clustering Using Hybrid SocioEvolutionary Algorithms." International Journal of Information Engineering and Electronic Business 8, no. 4 (July 8, 2016): 43–53. http://dx.doi.org/10.5815/ijieeb.2016.04.06.

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43

Wang, Lijuan, Zhifeng Hao, Ruichu Cai, and Wen Wen. "Enhanced soft subspace clustering through hybrid dissimilarity." Journal of Intelligent & Fuzzy Systems 29, no. 4 (October 23, 2015): 1395–405. http://dx.doi.org/10.3233/ifs-141517.

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44

Xiao, Jin, Yuhang Tian, Ling Xie, Xiaoyi Jiang, and Jing Huang. "A Hybrid Classification Framework Based on Clustering." IEEE Transactions on Industrial Informatics 16, no. 4 (April 2020): 2177–88. http://dx.doi.org/10.1109/tii.2019.2933675.

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45

Iván, Gábor, Zoltán Szabadka, and Vince Grolmusz. "A hybrid clustering of protein binding sites." FEBS Journal 277, no. 6 (February 10, 2010): 1494–502. http://dx.doi.org/10.1111/j.1742-4658.2010.07578.x.

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46

LIN, C. Y., J. Y. LIOU, and Y. J. YANG. "HYBRID MULTIMODAL OPTIMIZATION WITH CLUSTERING GENETIC STRATEGIES." Engineering Optimization 30, no. 3-4 (March 1998): 263–80. http://dx.doi.org/10.1080/03052159808941247.

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47

Byrne, Evan, Antoine Chatalic, Remi Gribonval, and Philip Schniter. "Sketched Clustering via Hybrid Approximate Message Passing." IEEE Transactions on Signal Processing 67, no. 17 (September 1, 2019): 4556–69. http://dx.doi.org/10.1109/tsp.2019.2924585.

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48

Baruque, Bruno, Santiago Porras, and Emilio Corchado. "Hybrid Classification Ensemble Using Topology-preserving Clustering." New Generation Computing 29, no. 3 (July 2011): 329–44. http://dx.doi.org/10.1007/s00354-011-0306-x.

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49

Ismail, M. A., and M. S. Kamel. "Multidimensional data clustering utilizing hybrid search strategies." Pattern Recognition 22, no. 1 (January 1989): 75–89. http://dx.doi.org/10.1016/0031-3203(89)90040-x.

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50

Tvrdík, Josef, and Ivan Křivý. "Hybrid differential evolution algorithm for optimal clustering." Applied Soft Computing 35 (October 2015): 502–12. http://dx.doi.org/10.1016/j.asoc.2015.06.032.

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