A hybrid clustering method based on the several diverse basic clustering and meta-clustering aggregation technique

In hybrid clustering, several basic clustering is first generated and then for the clustering aggregation, a function is used in order to create a final clustering that is similar to all the basic clustering as much as possible. The input of this function is all basic clustering and its output is a clustering called clustering agreement. However, this claim is correct if some conditions are met. This study has provided a hybrid clustering method. This study has used the basic k-means clustering method as a basic cluster. Also, this study has increased the diversity of consensus by adopting some measures. Here, the aggregation process of the basic clusters is done by the meta-clustering technique, where the primary clusters are re-clustered to form the final clusters. The proposed hybrid clustering method has the advantages of k-means, its high speed, as well as it does not have its major weaknesses, the inability to detect non-spherical and non-uniform clusters. In the empirical studies, we have evaluated the proposed hybrid clustering method with other up-to-date and robust clustering methods on the different datasets and compared them. According to the simulation results, the proposed hybrid clustering method is stronger than other clustering methods.

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.
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.
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. .
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.
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. .
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.
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. .
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.
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. .
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. .
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. .
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. .
Hamidi, S. S., E. Akbari, and H. Motameni. 2019. Consensus clustering algorithm based on the automatic partitioning similarity graph. Data & Knowledge Engineering 124:101754. .
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. .
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.
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. .
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. .
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. .
Jain, A. K. 2010. Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31 (8):651–66. .
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. .
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. .
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.
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. .
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. .
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. .
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. .
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.
Ng, A., M. Jordan, and Y. Weiss. 2001. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 14:849–56.
Nguyen, N., and R. Caruana. 2007. Consensus clusterings. In Seventh IEEE International Conference on Data Mining (ICDM 2007), 607–12. New York: IEEE. .
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. .
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. .
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. .
Rodriguez, A., and A. Laio. 2014. Clustering by fast search and find of density peaks. Science (New York, N.Y.) 344 (6191):1492–6. .
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. .
Strehl, A., and J. Ghosh. 2002. Cluster ensembles – A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3:583–617.
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. .
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. .
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. .
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. .
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. .
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. .
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. .
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.
Yang, W., Y. Zhang, H. Wang, P. Deng, and T. Li. 2021. Hybrid genetic model for clustering ensemble. Knowledge-Based Systems 231:107457. .
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.
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. .
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. .
Zhou, Z. H., and W. Tang. 2006. Clusterer ensemble. Knowledge-Based Systems 19 (1):77–83. .
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.