HAD-UMAP: Hybrid Attribute Data Set with Uniform Manifold Approximation and Projection for Adaptive Spectral Clustering

Qixia  Shen; Yanle  Song

doi:10.6180/jase.202603_29(4).0019

HAD-UMAP: Hybrid Attribute Data Set with Uniform Manifold Approximation and Projection for Adaptive Spectral Clustering

Research Categories

Qixia Shen¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Yanle Song¹

¹Department of Basic Course, Zhengzhou University of Science and Technology, Zhengzhou 450064, China

Received: June 25, 2025
Accepted: August 3, 2025
Publication Date: August 16, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202603_29(4).0019

In the clustering task, there are many mature algorithms. Due to the high dimension of the current data, if the appropriate dimensionality reduction is not carried out, the noise of the data will easily affect the accuracy of the algorithm. Meanwhile, to solve the attribute skew problem of spectral clustering algorithm when processing hybrid attribute data set, and the artificial selection problem of scale parameters in Gaussian kernel function, this paper proposes a novel hybrid attribute data set with uniform manifold approximation and projection (UMAP)for adaptive spectral clustering. UMAP method is used to reduce the dimensionality of the original data, and regularization spectral clustering algorithm is used to decompose the spectrum. This new method improves the traditional classification attribute similarity measure by calculating the information entropy of numerical attribute and classification attribute and obtaining the balance difference factor. In Gaussian kernel function, the neighborhood radius of each sample is calculated by using shared natural neighbors, and the scale parameters are solved adaptively. Finally, the similarity matrix of hybrid attribute samples is constructed by kernel function for spectral clustering. The experimental results show that proposed algorithm is better than four common hybrid attribute data clustering algorithms in ACC, ARI and NMI indexes. The clustering results are more stable. The new algorithm can effectively solve the attribute skew problem, especially the real distribution information of the hybrid attribute data set can be found completely adaptively, and the clustering efficiency is significantly improved.

Keywords: Hybrid attribute data set; adaptive spectral clustering; UMAP; information entropy; similarity matrix

[1] G. J. Oyewole and G. A. Thopil, (2023) “Data clustering: application and trends" Artificial intelligence review 56(7): 6439–6475. DOI: 10.1007/s10462-022 -10325-y.
[2] J. Cai, J. Hao, H. Yang, X. Zhao, and Y. Yang, (2023) “Areview on semi-supervised clustering" Information Sciences 632: 164–200. DOI: 10.1016/j.ins.2023.02.088.
[3] I. Barrio-Hernandez, J. Yeo, J. Jänes, M. Mirdita, C. L. Gilchrist, T. Wein, M. Varadi, S. Velankar, P. Beltrao, and M.Steinegger, (2023) “Clustering predicted structures at the scale of the known protein universe" Nature 622(7983): 637–645. DOI: 10.1038/s41586-023-06510-w.
[4] K.Taha,(2023)“Semi-supervisedandun-supervised clustering: A review and experimental evaluation" Information Systems 114: 102178. DOI: 10.1016/j.is.2023.102178.
[5] S. Yin, H. Li, D. Liu, and S. Karim, (2020) “Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation" Multimedia tools and applications 79(41): 31049–31068. DOI: 10.1007/s11042-020-09640-9.
[6] S. M. Miraftabzadeh, C. G. Colombo, M. Longo, and F. Foiadelli, (2023) “K-means and alternative clustering methods in modern power systems" Ieee Access 11: 119596–119633. DOI: 10.1109/ACCESS.2023.3327640.
[7] Y. Liu, X. Yang, S. Zhou, X. Liu, S. Wang, K. Liang, W. Tu, and L. Li, (2023) “Simple contrastive graph clustering" IEEE Transactions on Neural Networks and Learning Systems 35(10): 13789–13800. DOI: 10.1109/TNNLS.2023.3271871.
[8] A. M.Bagirov, R. M. Aliguliyev, and N. Sultanova, (2023) “Finding compact and well-separated clusters: Clustering using silhouette coefficients" Pattern Recognition 135: 109144. DOI: 10.1016/j.patcog.2022.109144.
[9] J.Yu,H.Li,andD.Liu,(2020) “Modified immuneevolutionary algorithm for medical data clustering and feature extraction under cloud computing environment" Journal of healthcare engineering 2020(1): 1051394. DOI: 10.1155/2020/1051394.
[10] J. Xie, W. Kong, S. Xia, G. Wang, and X. Gao, (2023) “Anefficient spectral clustering algorithm based on granular-ball" IEEE Transactions on Knowledge and Data Engineering 35(9): 9743–9753. DOI: 10.1109/TKDE.2023.3249475.
[11] S. Forouzandeh, K. Berahmand, R. Sheikhpour, and Y. Li, (2023) “A new method for recommendation based on embedding spectral clustering in heterogeneous networks (RESCHet)" Expert Systems with Applications 231: 120699. DOI: 10.1016/j.eswa.2023.120699.
[12] C. Gao, W. Chen, F. Nie, W. Yu, and Z. Wang, (2024) “Spectral clustering with linear embedding: A discrete clustering method for large-scale data" Pattern Recognition 151: 110396. DOI: 10.1016/j.patcog.2024.110396.
[13] J. H. Priyanka and N. Parveen, (2024) “DeepSkillNER: an automatic screening and ranking of resumes using hybrid deep learning and enhanced spectral clustering approach" Multimedia Tools and Applications 83(16): 47503–47530. DOI: 10.1007/s11042-023-17264-y.
[14] Q. Zheng, (2024) “Flexible and parameter-free graph learning for multi-view spectral clustering" IEEE Trans actions on Circuits and Systems for Video Technology 34(9): 8966–8971. DOI: 10.1109/TCSVT.2024.3382761.
[15] C. Wang, Z.Gu,and J.-M.Wei,(2024)“Spectralclustering and embedding with inter-class topology-preserving" Knowledge-Based Systems 284: 111278. DOI: 10.1016/j.knosys.2023.111278.
[16] Y. Sun, S. Yin, H. Li, L. Teng, and S. Karim, (2019) “GPOGC:Gaussianpigeon-oriented graph clustering algorithm for social networks cluster" IEEE Access 7: 99254 99262. DOI: 10.1109/ACCESS.2019.2926816.
[17] Y. Liu, D. Chen, S. Fu, P. T. Mathiopoulos, M. Sui, J. Na, andJ. Peethambaran, (2024) “Segmentation of individual tree points by combining marker-controlled water shed segmentation and spectral clustering optimization" Remote Sensing 16(4): 610. DOI: 10.3390/rs16040610.
[18] K. P. Kumar, B. H. Kumar, and A. Bhuvanesh, (2024) “Spectral clustering algorithm based web mining and quadratic support vector machine for learning style pre diction in E-learning platform" Measurement: Sensors 31: 100962. DOI: 10.1016/j.measen.2023.100962.
[19] T. Yang, C.-D. Wang, J. Guo, X. Li, M.-S. Chen, S. Dang, and H. Chen, (2025) “Triplets-based large-scale multi-view spectral clustering" Information Fusion 121: 103134. DOI: 10.1016/j.inffus.2025.103134.
[20] J. Deng, D. Huang, Y. Ding, Y. Zhu, B. Jing, and B. Zhang, (2024) “Subsampling spectral clustering for stochastic block models in large-scale networks" Computational Statistics & Data Analysis 189: 107835. DOI: 10.1016/j.csda.2023.107835.
[21] X. Peng, L. Zhang, and Z. Yi. “Scalable sparse sub space clustering”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2013, 430 437. DOI: 10.1109/CVPR.2013.62.
[22] W. Wang, Z. Jiang, J. Zhang, and L. Yang. “Load swing suppression and path tracking control of the quadrotor with non-centroid suspended payload in windy environments”. In: 2023 42nd Chinese Control Conference (CCC). IEEE. 2023, 750–757. DOI: 10.23919/CCC58697.2023.10240238.
[23] Z. Wu, M. Yin, Y. Zhou, X. Fang, and S. Xie, (2018) “Robust spectral subspace clustering based on least square regression" Neural processing letters 48(3): 1359 1372. DOI: 10.1007/s11063-017-9726-z.
[24] P. O. Brown, M. C. Chiang, S. Guo, Y. Jin, C. K. Leung, E. L. Murray, A. G. Pazdor, and A. Cuzzocrea. “Mahalanobis distance based k-means clustering”. In: International Conference on Big Data Analytics and Knowledge Discovery. Springer. 2022, 256–262. DOI: 10.1007/978-3-031-12670-3_23.
[25] C. Hou, L.-L. Zeng, and D. Hu, (2018) “Safe classification with augmented features" IEEE transactions on pattern analysis and machine intelligence 41(9): 2176–2192. DOI: 10.1109/TPAMI.2018.2849378.
[26] S. S. Salleh, N. A. A. Aziz, D. Mohamad, and M. Omar, (2012) “Combining mahalanobis and jaccard distance to overcome similarity measurement constriction on geometrical shapes" International Journal of Computer Science Issues (IJCSI) 9(4): 124. DOI: 10.1109/uksim.2011.67.
[27] W. Zhou, L. Wang, X. Han, M. Parmar, and M. Li, (2023) “A novel density deviation multi-peaks automatic clustering algorithm" Complex & Intelligent Systems 9(1): 177–211. DOI: 10.1007/s40747-022-00798-3.
[28] J. Zhao, G. Wang, J.-S. Pan, T. Fan, and I. Lee, (2023) “Density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets" Pattern Recognition 139: 109406. DOI: 10.1016/j.patcog.2023.109406.
[29] J. Healy and L. McInnes, (2024) “Uniform manifold ap proximation and projection" Nature Reviews Methods Primers 4(1): 82. DOI: 10.1038/s43586-024-00363-x.
[30] A. Li, S. Mei, C. Feng, T. Gao, and H. Huang, (2025) “Deep spectral clustering network for incomplete multiview clustering" Engineering Applications of Artificial Intelligence 148: 110387. DOI: 10.1016/j. engappai.2025.110387.
[31] H. Zhou, Z. Wang, H. Chen, and X. Wang, (2024) “A novel spectral clustering algorithm based on neighbor relation and Gaussian kernel function with only one parameter" Soft Computing 28(2): 981–989. DOI: 10.1007/ s00500-023-09309-z.
[32] Q. Yu, L. Jia, Y. Shao, J. He, J. Wang, X. Yuan, M. Huan, and Y. Yang, (2025) “A local adaptive fuzzy spectral clustering method for robust and practical clustering" Scientific Reports 15(1): 7833. DOI: 10.1038/s41598 025-91812-4.
[33] A. Y. Zhang and H. Y. Zhou, (2024) “Leave-one-out singular subspace perturbation analysis for spectral clustering" The Annals of Statistics 52(5): 2004–2033. DOI: 10.1214/24-aos2418.
[34] A. K. Moorthy, V. V. Saradhi, and B. Prasad, (2025) “Ensuring Fairness in Spectral Clustering via Disparate Impact-Based Graph Construction" IEEE Transactions on Artificial Intelligence 6(8): 2342–2352. DOI: 10.1109/TAI.2025.3545800.