An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder
Source abstract
Abstract
Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC’s validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Economic Research Institute of State Grid Zhejiang Electric Power Company, Hangzhou 102209, China
Funding
National Key R&D Program of China
References (28)
Show source-provided references
1. A general multiobjective clustering approach based on multiple distance measures10.1109/ACCESS.2018.2860791 ↗
2. A survey of multiobjective evolutionary clustering10.1145/2742642 ↗
3. Abu Khurma, R., and Aljarah, I. (2021). Evolutionary Data Clustering: Algorithms and Applications, Springer.
4. A survey of evolutionary algorithms for clustering10.1109/TSMCC.2008.2007252 ↗
5. Multiobjective clustering with metaheuristic: Current trends and methods in image segmentation10.1049/iet-ipr.2010.0122 ↗
6. Morimoto, C.Y., Pozo, A., and de Souto, M.C. (2021). A Review of Evolutionary Multi-objective Clustering Approaches. arXiv.
7. Multi-clustering via evolutionary multi-objective optimization10.1016/j.ins.2018.03.047 ↗
8. A multi-clustering method based on evolutionary multiobjective optimization with grid decomposition10.1016/j.swevo.2020.100691 ↗
9. Evolutionary multi-objective automatic clustering enhanced with quality metrics and ensemble strategy10.1016/j.knosys.2019.105018 ↗
10. An improved and more scalable evolutionary approach to multiobjective clustering
11. Zhu, S., Xu, L., and Cao, L. (2018, January 15–19). A study of automatic clustering based on evolutionary many-objective optimization. Proceedings of the Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan.10.1145/3205651.3205759 ↗
12. Bechikh, S., Elarbi, M., Hung, C.C., Hamdi, S., and Said, L.B. (2019, January 10–13). A Hybrid Evolutionary Algorithm with Heuristic Mutation for Multi-objective Bi-clustering. Proceedings of the 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand.10.1109/CEC.2019.8790309 ↗
13. Di Nuovo, A.G., Palesi, M., and Catania, V. (2007, January 23–26). Multi-objective evolutionary fuzzy clustering for high-dimensional problems. Proceedings of the 2007 IEEE International Fuzzy Systems Conference, London, UK.10.1109/FUZZY.2007.4295660 ↗
14. Song, C., Liu, F., Huang, Y., Wang, L., and Tan, T. (2013, January 20–23). Auto-encoder based data clustering. Proceedings of the 18th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Havana, Cuba.10.1007/978-3-642-41822-8_15 ↗
15. Huang, P., Huang, Y., Wang, W., and Wang, L. (2014, January 24–28). Deep embedding network for clustering. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.10.1109/ICPR.2014.272 ↗
16. Xie, J., Girshick, R., and Farhadi, A. (2016, January 19–24). Unsupervised deep embedding for clustering analysis. Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, USA.
17. Yang, B., Fu, X., Sidiropoulos, N.D., and Hong, M. (2017, January 6–11). Towards k-means-friendly spaces: Simultaneous deep learning and clustering. Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia.
18. Yang, X., Deng, C., Zheng, F., Yan, J., and Liu, W. (2019, January 15–20). Deep spectral clustering using dual autoencoder network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00419 ↗
19. Unsupervised deep clustering via adaptive GMM modeling and optimization10.1016/j.neucom.2020.12.082 ↗
20. Zhu, D., Chen, S., Ma, X., and Du, R. (2020). Adaptive Graph Convolution Using Heat Kernel for Attributed Graph Clustering. Appl. Sci., 10.10.3390/app10041473 ↗
21. Genetic K-means algorithm10.1109/3477.764879 ↗
22. Özyer, T., Liu, Y., Alhajj, R., and Barker, K. (2004, January 20–22). Multi-objective genetic algorithm based clustering approach and its application to gene expression data. Proceedings of the Third International Conference on Advances in Information Systems, Izmir, Turkey.10.1007/978-3-540-30198-1_46 ↗
23. Nair, V., and Hinton, G.E. (2010, January 21–24). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel.
24. Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000, January 18–20). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, Paris, France.10.1007/3-540-45356-3_83 ↗
25. Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data10.1093/bioinformatics/17.9.763 ↗
26. Locally consistent concept factorization for document clustering10.1109/TKDE.2010.165 ↗
27. Kingma, D., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
28. MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Statistical Laboratory of the University of California, Berkeley, CA, USA.