A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique

References

• Abapour, N., A. Shafiesabet, and R. Mahboub. 2021. A novel security based routing method using ant colony optimization algorithms and RPL protocol in the IoT networks. International Journal of Electrical and Computer Sciences 3 (1):1–9.
   Google Scholar
• Azimi, J., and X. Fern. 2009. Adaptive cluster ensemble selection. In Twenty-First International Joint Conference on Artificial Intelligence, Vol. 9, 992–7, California, USA, July 11–17.
   Google Scholar
• Bai, L., J. Liang, and F. Cao. 2020. A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters. Information Fusion 61:36–47. doi:10.1016/j.inffus.2020.03.009.
   Web of Science ®Google Scholar
• Berahmand, K., E. Nasiri, R. Pir Mohammadiani, and Y. Li. 2021. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding. Computers in Biology and Medicine 138:104933.
   PubMed Web of Science ®Google Scholar
• Bouyer, A, and A. Hatamlou. 2018. An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Applied Soft Computing 67:172–82. doi:10.1016/j.asoc.2018.03.011.
   Web of Science ®Google Scholar
• Ester, M., H. P. Kriegel, J. Sander, and X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD-96, 226–31, Portland, Oregon, USA, August 2–4.
   Google Scholar
• Forouzandeh, S., K. Berahmand, E. Nasiri, and M. Rostami. 2021. A hotel recommender system for tourists using the Artificial Bee Colony Algorithm and Fuzzy TOPSIS Model: A case study of tripadvisor. International Journal of Information Technology & Decision Making 20 (1):399–429. doi:10.1142/S0219622020500522.
   Web of Science ®Google Scholar
• Fred, A. L., and A. K. Jain. 2005. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (6):835–50.
   PubMed Web of Science ®Google Scholar
• Ghobaei-Arani, M. 2021. A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems. Soft Computing 25 (5):3813–30. doi:10.1007/s00500-020-05409-2.
   Web of Science ®Google Scholar
• Ghobaei-Arani, M., and A. Shahidinejad. 2021. An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. The Journal of Supercomputing 77 (1):711–50. doi:10.1007/s11227-020-03296-w.
   Web of Science ®Google Scholar
• Golalipour, K., E. Akbari, S. S. Hamidi, M. Lee, and R. Enayatifar. 2021. From clustering to clustering ensemble selection: A review. Engineering Applications of Artificial Intelligence 104:104388. doi:10.1016/j.engappai.2021.104388.
   Web of Science ®Google Scholar
• Golrou, A., A. Sheikhani, A. M. Nasrabadi, and M. R. Saebipour. 2018. Enhancement of sleep quality and stability using acoustic stimulation during slow wave sleep. International Clinical Neuroscience Journal 5 (4):126–34. doi:10.15171/icnj.2018.25.
   Google Scholar
• Hamidi, S. S., E. Akbari, and H. Motameni. 2019. Consensus clustering algorithm based on the automatic partitioning similarity graph. Data & Knowledge Engineering 124:101754. doi:10.1016/j.datak.2019.101754.
   Web of Science ®Google Scholar
• Hansen, P., and N. Mladenović. 2001. J-means: A new local search heuristic for minimum sum of squares clustering. Pattern Recognition 34 (2):405–13. doi:10.1016/S0031-3203(99)00216-2.
   Web of Science ®Google Scholar
• Huang, D., C. D. Wang, and J. H. Lai. 2017. LWMC: A locally weighted meta-clustering algorithm for ensemble clustering. In International Conference on Neural Information Processing, 167–76. Cham: Springer.
   Google Scholar
• Huang, D., C. D. Wang, J. S. Wu, J. H. Lai, and C. K. Kwoh. 2020. Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge and Data Engineering 32 (6):1212–26. doi:10.1109/TKDE.2019.2903410.
   Web of Science ®Google Scholar
• Iam-On, N., T. Boongoen, S. Garrett, and C. Price. 2011. A link-based approach to the cluster ensemble problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (12):2396–409. doi:10.1109/TPAMI.2011.84.
   PubMed Web of Science ®Google Scholar
• Jadidi, A., and M. R. Dizadji. 2021. Node clustering in binary asymmetric stochastic block model with noisy label attributes via SDP. In 2021 International Conference on Smart Applications, Communications and Networking (SmartNets), 1–6. New York: IEEE. doi:10.1109/SmartNets50376.2021.9555421.
   Google Scholar
• Jain, A. K. 2010. Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31 (8):651–66. doi:10.1016/j.patrec.2009.09.011.
   Web of Science ®Google Scholar
• Jiang, H., S. Yi, J. Li, F. Yang, and X. Hu. 2010. Ant clustering algorithm with K-harmonic means clustering. Expert Systems with Applications 37 (12):8679–84. doi:10.1016/j.eswa.2010.06.061.
   Web of Science ®Google Scholar
• Khedairia, S., and M. T. Khadir. 2022. A multiple clustering combination approach based on iterative voting process. Journal of King Saud University – Computer and Information Sciences 34 (1):1370–80. doi:10.1016/j.jksuci.2019.09.013.
   Web of Science ®Google Scholar
• Li, F., Y. Qian, and J. Wang. 2021. GoT: A growing tree model for clustering ensemble. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 8349–56, California, USA, February 2–9.
   Google Scholar
• Li, T., A. Rezaeipanah, and E. M. T. El Din. 2022. An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement. Journal of King Saud University – Computer and Information Sciences 34 (6):3828–42. doi:10.1016/j.jksuci.2022.04.010.
   Web of Science ®Google Scholar
• Ma, T., Z. Zhang, L. Guo, X. Wang, Y. Qian, and N. Al-Nabhan. 2021. Semi-supervised Selective Clustering Ensemble based on constraint information. Neurocomputing 462:412–25. doi:10.1016/j.neucom.2021.07.056.
   Web of Science ®Google Scholar
• Mojarad, M., F. Sarhangnia, A. Rezaeipanah, H. Parvin, and S. Nejatian. 2021. Modeling hereditary disease behavior using an innovative similarity criterion and ensemble clustering. Current Bioinformatics 16 (5):749–64. doi:10.2174/1574893616999210128175715.
   Web of Science ®Google Scholar
• Movahhed Neya, N., S. Saberi, and B. Rezaie. 2022. Design of an adaptive controller to capture maximum power from a variable speed wind turbine system without any prior knowledge of system parameters. Transactions of the Institute of Measurement and Control 44 (3):609–19. doi:10.1177/01423312211039041.
   Web of Science ®Google Scholar
• Nasiri, E., K. Berahmand, Z. Samei, and Y. Li. 2022. Impact of centrality measures on the common neighbors in link prediction for multiplex networks. Big Data 10 (2):138–50.
   PubMed Web of Science ®Google Scholar
• Ng, A., M. Jordan, and Y. Weiss. 2001. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 14:849–56.
   Google Scholar
• Nguyen, N., and R. Caruana. 2007. Consensus clusterings. In Seventh IEEE International Conference on Data Mining (ICDM 2007), 607–12. New York: IEEE. doi:10.1109/ICDM.2007.73.
   Google Scholar
• Niu, H., N. Khozouie, H. Parvin, H. Alinejad-Rokny, A. Beheshti, and M. R. Mahmoudi. 2020. An ensemble of locally reliable cluster solutions. Applied Sciences 10 (5):1891. doi:10.3390/app10051891.
   Google Scholar
• Rezaeipanah, A., P. Amiri, H. Nazari, M. Mojarad, and H. Parvin. 2021. An energy-aware hybrid approach for wireless sensor networks using re-clustering-based multi-hop routing. Wireless Personal Communications 120 (4):3293–314. doi:10.1007/s11277-021-08614-w.
   Web of Science ®Google Scholar
• Rezaeipanah, A., H. Nazari, and G. Ahmadi. 2019. A hybrid approach for prolonging lifetime of wireless sensor networks using genetic algorithm and online clustering. Journal of Computing Science and Engineering 13 (4):163–74. doi:10.5626/JCSE.2019.13.4.163.
   Google Scholar
• Rodriguez, A., and A. Laio. 2014. Clustering by fast search and find of density peaks. Science (New York, N.Y.) 344 (6191):1492–6. doi:10.1126/science.1242072.
   PubMed Web of Science ®Google Scholar
• Shahidinejad, A., M. Ghobaei-Arani, and L. Esmaeili. 2020. An elastic controller using Colored Petri Nets in cloud computing environment. Cluster Computing 23 (2):1045–71. doi:10.1007/s10586-019-02972-8.
   Web of Science ®Google Scholar
• Strehl, A., and J. Ghosh. 2002. Cluster ensembles – A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3:583–617.
   Google Scholar
• Sun, S., S. Wang, G. Zhang, and J. Zheng. 2018. A decomposition-clustering-ensemble learning approach for solar radiation forecasting. Solar Energy 163:189–99. doi:10.1016/j.solener.2018.02.006.
   Web of Science ®Google Scholar
• Tan, H., Y. Tian, L. Wang, and G. Lin. 2020. Name disambiguation using meta clusters and clustering ensemble. Journal of Intelligent & Fuzzy Systems 38 (2):1559–68. doi:10.3233/JIFS-179519.
   Web of Science ®Google Scholar
• Topchy, A., A. K. Jain, and W. Punch. 2005. Clustering ensembles: Models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (12):1866–81. doi:10.1109/TPAMI.2005.237.
   PubMed Web of Science ®Google Scholar
• Trik, M., A. M. N. G. Molk, F. Ghasemi, and P. Pouryeganeh. 2022. A hybrid selection strategy based on traffic analysis for improving performance in networks on chip. Journal of Sensors 2022:1–19. doi:10.1155/2022/3112170.
   Web of Science ®Google Scholar
• Trik, M., S. Pour Mozaffari, and A. M. Bidgoli. 2021. Providing an adaptive routing along with a hybrid selection strategy to increase efficiency in NoC-based neuromorphic systems. Computational Intelligence and Neuroscience 2021:8338903. doi:10.1155/2021/8338903.
   PubMed Web of Science ®Google Scholar
• Walid, W., M. Awais, A. Ahmed, G. Masera, and M. Martina. 2021. Real-time implementation of fast discriminative scale space tracking algorithm. Journal of Real-Time Image Processing 18 (6):2347–60. doi:10.1007/s11554-021-01119-6.
   Web of Science ®Google Scholar
• Wei, S., Z. Li, and C. Zhang. 2018. Combined constraint-based with metric-based in semi-supervised clustering ensemble. International Journal of Machine Learning and Cybernetics 9 (7):1085–100. doi:10.1007/s13042-016-0628-6.
   Web of Science ®Google Scholar
• Wei, Y., S. Sun, J. Ma, S. Wang, and K. K. Lai. 2019. A decomposition clustering ensemble learning approach for forecasting foreign exchange rates. Journal of Management Science and Engineering 4 (1):45–54.
   Google Scholar
• Yang, W., Y. Zhang, H. Wang, P. Deng, and T. Li. 2021. Hybrid genetic model for clustering ensemble. Knowledge-Based Systems 231:107457. doi:10.1016/j.knosys.2021.107457.
   Web of Science ®Google Scholar
• Zhang, B., M. Hsu, and U. Dayal. 2000. K-harmonic means-a spatial clustering algorithm with boosting. In International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining, 31–45. Berlin, Heidelberg: Springer.
   Google Scholar
• Zhao, Q., Y. Zhu, D. Wan, Y. Yu, and Y. Lu. 2019. Similarity analysis of small-and medium-sized watersheds based on clustering ensemble model. Water 12 (1):69. doi:10.3390/w12010069.
   Web of Science ®Google Scholar
• Zheng, Y., Z. Long, C. Wei, and H. Wang. 2021. Particle swarm optimization for clustering ensemble. In 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 385–91. New York: IEEE. doi:10.1109/ISKE54062.2021.9755338.
   Google Scholar
• Zhou, Z. H., and W. Tang. 2006. Clusterer ensemble. Knowledge-Based Systems 19 (1):77–83. doi:10.1016/j.knosys.2005.11.003.
   Web of Science ®Google Scholar
• Zhu, X., B. Fei, D. Liu, and W. Bao. 2021. Adaptive clustering ensemble method based on uncertain entropy decision-making. In 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 61–7. New York: IEEE. doi:10.1109/TrustCom53373.2021.00026.
   Google Scholar