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Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433–459.AbdiH.WilliamsL. J. (2010). Principal component analysis. , 2(4), 433–459.Search in Google Scholar
Achterberg, T., Bixby, R. E., Gu, Z., Rothberg, E., & Weninger, D. (2020). Presolve reductions in mixed integer programming. INFORMS Journal on Computing, 32(2), 473–506.AchterbergT.BixbyR. E.GuZ.RothbergE.WeningerD. (2020). Presolve reductions in mixed integer programming. , 32(2), 473–506.Search in Google Scholar
Alexiou, A., Mason, K., Fahy, K., Taylor-Robinson, D., & Barr, B. (2021). Assessing the impact of funding cuts to local housing services on drug and alcohol related mortality: a longitudinal study using area-level data in England. International Journal of Housing Policy, 1–19.AlexiouA.MasonK.FahyK.Taylor-RobinsonD.BarrB. (2021). Assessing the impact of funding cuts to local housing services on drug and alcohol related mortality: a longitudinal study using area-level data in England. , 1–19.Search in Google Scholar
Baumann, F. (2021). The next frontier—human development and the anthropocene: UNDP human development report 2020. Environment: Science and Policy for Sustainable Development, 63(3), 34–40.BaumannF. (2021). The next frontier—human development and the anthropocene: UNDP human development report 2020. , 63(3), 34–40.Search in Google Scholar
Bayati, M., Noroozi, R., Ghanbari-Jahromi, M., & Jalali, F. S. (2022). Inequality in the distribution of Covid-19 vaccine: a systematic review. International journal for equity in health, 21(1), 1–9.BayatiM.NorooziR.Ghanbari-JahromiM.JalaliF. S. (2022). Inequality in the distribution of Covid-19 vaccine: a systematic review. , 21(1), 1–9.Search in Google Scholar
Belkin, M., & Niyogi, P. (2001). Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in neural information processing systems, 14.BelkinM.NiyogiP. (2001). Laplacian eigenmaps and spectral techniques for embedding and clustering. , 14.Search in Google Scholar
Bhadra, A., Datta, J., Polson, N. G., & Willard, B. (2019). Lasso meets horseshoe. Statistical Science, 34(3), 405–427.BhadraA.DattaJ.PolsonN. G.WillardB. (2019). Lasso meets horseshoe. , 34(3), 405–427.Search in Google Scholar
Can-can, Y., Shuai, T., Shan, T., & Wen-tao, Z. (2022). UMAP-Assisted Fuzzy C-Clustering Method for Recognition of Terahertz Spectrum. Spectroscopy and Spectral Analysis, 42(9), 2694–2701.Can-canY.ShuaiT.ShanT.Wen-taoZ. (2022). UMAP-Assisted Fuzzy C-Clustering Method for Recognition of Terahertz Spectrum. , 42(9), 2694–2701.Search in Google Scholar
Cortés, P., Muñuzuri, J., Onieva, L., & Guadix, J. (2018). A discrete particle swarm optimisation algorithm to operate distributed energy generation networks efficiently. International Journal of Bio-Inspired Computation, 12(4), 226–235.CortésP.MuñuzuriJ.OnievaL.GuadixJ. (2018). A discrete particle swarm optimisation algorithm to operate distributed energy generation networks efficiently. , 12(4), 226–235.Search in Google Scholar
Currie, J., Boyce, T., Evans, L., Luker, M., Senior, S., Hartt, M.,... Humphreys, C. (2021). Life expectancy inequalities in Wales before COVID-19: an exploration of current contributions by age and cause of death and changes between 2002 and 2018. Public Health, 193, 48–56.CurrieJ.BoyceT.EvansL.LukerM.SeniorS.HarttM.HumphreysC. (2021). Life expectancy inequalities in Wales before COVID-19: an exploration of current contributions by age and cause of death and changes between 2002 and 2018. , 193, 48–56.Search in Google Scholar
Deutelmoser, H., Scherer, D., Brenner, H., Waldenberger, M., Study, I., Suhre, K.,... Lorenzo Bermejo, J. (2021). Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data. Briefings in Bioinformatics, 22(4), bbaa230.DeutelmoserH.SchererD.BrennerH.WaldenbergerM.StudyI.SuhreK.Lorenzo BermejoJ. (2021). Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data. , 22(4), bbaa230.Search in Google Scholar
Ding, C., & He, X. F. (2004). K-means clustering via principal component analysis. Paper presented at the Proceedings of the twenty-first international conference on Machine learning.DingC.HeX. F. (2004). . Paper presented at the Proceedings of the twenty-first international conference on Machine learning.Search in Google Scholar
Espadoto, M., Martins, R. M., Kerren, A., Hirata, N. S., & Telea, A. C. (2019). Toward a quantitative survey of dimension reduction techniques. IEEE transactions on visualization and computer graphics, 27(3), 2153–2173.EspadotoM.MartinsR. M.KerrenA.HirataN. S.TeleaA. C. (2019). Toward a quantitative survey of dimension reduction techniques. , 27(3), 2153–2173.Search in Google Scholar
Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I.,... Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.EzugwuA. E.IkotunA. M.OyeladeO. O.AbualigahL.AgushakaJ. O.EkeC. I.AkinyeluA. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. , 110, 104743.Search in Google Scholar
Fahim, A. (2023). Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities. Computers, Materials & Continua, 75(2), , 3695–3712.FahimA. (2023). Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities. , 75(2), , 3695–3712.Search in Google Scholar
Farhud, D. D. (2022). Hypothetical Strategies of Gene and Environmental Influence on Life Expectancy: A Brief Review. Iranian Journal of Public Health, 51(11), 2382.FarhudD. D. (2022). Hypothetical Strategies of Gene and Environmental Influence on Life Expectancy: A Brief Review. , 51(11), 2382.Search in Google Scholar
Flegner, P., Kačur, J., Frančáková, R., Durdán, M., & Laciak, M. (2023). Application of Cluster Analysis for Classification of Vibration Signals from Drilling Stand Aggregates. Applied Sciences, 13(10), 6337.FlegnerP.KačurJ.FrančákováR.DurdánM.LaciakM. (2023). Application of Cluster Analysis for Classification of Vibration Signals from Drilling Stand Aggregates. , 13(10), 6337.Search in Google Scholar
Golalipour, K., Akbari, E., Hamidi, S. S., Lee, M., & Enayatifar, R. (2021). From clustering to clustering ensemble selection: A review. Engineering Applications of Artificial Intelligence, 104, 104388.GolalipourK.AkbariE.HamidiS. S.LeeM.EnayatifarR. (2021). From clustering to clustering ensemble selection: A review. , 104, 104388.Search in Google Scholar
Guha, S., Rastogi, R., & Shim, K. (2000). ROCK: A robust clustering algorithm for categorical attributes. Information systems, 25(5), 345–366.GuhaS.RastogiR.ShimK. (2000). ROCK: A robust clustering algorithm for categorical attributes. , 25(5), 345–366.Search in Google Scholar
Gupta, S., Zhang, Y., & Su, R. (2022). Urban traffic light scheduling for pedestrian – vehicle mixed-flow networks using discrete sine – cosine algorithm and its variants. Applied Soft Computing, 120, 108656.GuptaS.ZhangY.SuR. (2022). Urban traffic light scheduling for pedestrian – vehicle mixed-flow networks using discrete sine – cosine algorithm and its variants. , 120, 108656.Search in Google Scholar
Jia, W. K., Sun, M. L., Lian, J., & Hou, S. J. (2022). Feature dimensionality reduction: a review. Complex & Intelligent Systems, 8(3), 2663–2693.JiaW. K.SunM. L.LianJ.HouS. J. (2022). Feature dimensionality reduction: a review. , 8(3), 2663–2693.Search in Google Scholar
Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling: Sage.KruskalJ. B.WishM. (1978). : Sage.Search in Google Scholar
Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.LeeD. D.SeungH. S. (1999). Learning the parts of objects by non-negative matrix factorization. , 401(6755), 788–791.Search in Google Scholar
Li, X. D., Sun, D. F., & Toh, K. C. (2020). An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for linear programming. SIAM Journal on Optimization, 30(3), 2410–2440.LiX. D.SunD. F.TohK. C. (2020). An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for linear programming. , 30(3), 2410–2440.Search in Google Scholar
Lichtenberg, F. R. (2022). The effect of pharmaceutical innovation on longevity: Evidence from the US and 26 high-income countries. Economics & Human Biology, 46, 101124.LichtenbergF. R. (2022). The effect of pharmaceutical innovation on longevity: Evidence from the US and 26 high-income countries. , 46, 101124.Search in Google Scholar
Liu, W. H., Zeng, S., Wu, G. J., Li, H., & Chen, F. F. (2021). Rice seed purity identification technology using hyperspectral image with LASSO logistic regression model. Sensors, 21(13), 4384.LiuW. H.ZengS.WuG. J.LiH.ChenF. F. (2021). Rice seed purity identification technology using hyperspectral image with LASSO logistic regression model. , 21(13), 4384.Search in Google Scholar
McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426.McInnesL.HealyJ.MelvilleJ. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. .Search in Google Scholar
Nasiri, E., Berahmand, K., Rostami, M., & Dabiri, M. (2021). A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding. Computers in Biology and Medicine, 137, 104772.NasiriE.BerahmandK.RostamiM.DabiriM. (2021). A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding. , 137, 104772.Search in Google Scholar
Oyewole, G. J., & Thopil, G. A. (2023). Data clustering: Application and trends. Artificial Intelligence Review, 56(7), 6439–6475.OyewoleG. J.ThopilG. A. (2023). Data clustering: Application and trends. , 56(7), 6439–6475.Search in Google Scholar
Rani, R., Khurana, M., Kumar, A., & Kumar, N. (2022). Big data dimensionality reduction techniques in IoT: Review, applications and open research challenges. Cluster Computing, 25(6), 4027–4049.RaniR.KhuranaM.KumarA.KumarN. (2022). Big data dimensionality reduction techniques in IoT: Review, applications and open research challenges. , 25(6), 4027–4049.Search in Google Scholar
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500), 2323–2326.RoweisS. T.SaulL. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. , 290(5500), 2323–2326.Search in Google Scholar
Roy, S., Howlader, J., & Sanyal, G. (2022). A novel approach of data hiding in video using region selection and PCA. Multimedia Tools and Applications, 81(10), 14553–14571.RoyS.HowladerJ.SanyalG. (2022). A novel approach of data hiding in video using region selection and PCA. , 81(10), 14553–14571.Search in Google Scholar
Sawant, M., & Bhurchandi, K. M. (2022). Discriminative aging subspace learning for age estimation. Soft Computing, 26(18), 9189–9198.SawantM.BhurchandiK. M. (2022). Discriminative aging subspace learning for age estimation. , 26(18), 9189–9198.Search in Google Scholar
Sen, A. (1998). Mortality as an indicator of economic success and failure. The economic journal, 108(446), 1–25.SenA. (1998). Mortality as an indicator of economic success and failure. , 108(446), 1–25.Search in Google Scholar
Shuai, Y. (2022). A Full-Sample Clustering Model Considering Whole Process Optimization of Data. Big Data Research, 28, 100301.ShuaiY. (2022). A Full-Sample Clustering Model Considering Whole Process Optimization of Data. , 28, 100301.Search in Google Scholar
Song, X., Li, S. H., Qi, Z. Q., & Zhu, J. L. (2022). A spectral clustering algorithm based on attribute fluctuation and density peaks clustering algorithm. Applied Intelligence, 1–15.SongX.LiS. H.QiZ. Q.ZhuJ. L. (2022). A spectral clustering algorithm based on attribute fluctuation and density peaks clustering algorithm. ,1–15.Search in Google Scholar
Stephenson, W. (1935). Technique of factor analysis. Nature, 136(3434), 297.StephensonW. (1935). Technique of factor analysis. , 136(3434), 297.Search in Google Scholar
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. science, 290(5500), 2319–2323.TenenbaumJ. B.SilvaV. D.LangfordJ. C. (2000). A global geometric framework for nonlinear dimensionality reduction. , 290(5500), 2319–2323.Search in Google Scholar
Tian, P., Shen, H., & Abolfathi, A. (2022). Towards Efficient Ensemble Hierarchical Clustering with MapReduce-based Clusters Clustering Technique and the Innovative Similarity Criterion. Journal of Grid Computing, 20(4), 34.TianP.ShenH.AbolfathiA. (2022). Towards Efficient Ensemble Hierarchical Clustering with MapReduce-based Clusters Clustering Technique and the Innovative Similarity Criterion. , 20(4), 34.Search in Google Scholar
Ullah, B., Kamran, M., & Rui, Y. (2022). Predictive modeling of short-term rockburst for the stability of subsurface structures using machine learning approaches: T-SNE, K-Means clustering and XGBoost. Mathematics, 10(3), 449.UllahB.KamranM.RuiY. (2022). Predictive modeling of short-term rockburst for the stability of subsurface structures using machine learning approaches: T-SNE, K-Means clustering and XGBoost. , 10(3), 449.Search in Google Scholar
Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11).Van der MaatenL.HintonG. (2008). Visualizing data using t-SNE. , 9(11).Search in Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,... Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.VaswaniA.ShazeerN.ParmarN.UszkoreitJ.JonesL.GomezA. N.PolosukhinI. (2017). Attention is all you need. , 30.Search in Google Scholar
Wang, S. L., Li, Q., Zhao, C. F., Zhu, X. G., Yuan, H. N, & Dai, T. R. (2021). Extreme clustering – a clustering method via density extreme points. Information Sciences, 542, 24–39.WangS. L.LiQ.ZhaoC. F.ZhuX. G.YuanH. NDaiT. R. (2021). Extreme clustering – a clustering method via density extreme points. , 542, 24–39.Search in Google Scholar
Xia, J. Z., Zhang, Y. C., Song, J., Chen, Y., Wang, Y. H., & Liu, S. (2021). Revisiting dimensionality reduction techniques for visual cluster analysis: an empirical study. IEEE Transactions on Visualization and Computer Graphics, 28(1), 529–539.XiaJ. Z.ZhangY. C.SongJ.ChenY.WangY. H.LiuS. (2021). Revisiting dimensionality reduction techniques for visual cluster analysis: an empirical study. , 28(1), 529–539.Search in Google Scholar
Yang, L. J., Yan, L. L., Yang, X. H., Xin, X., & Xue, L. G. (2022). Bayesian nonnegative matrix factorization in an incremental manner for data representation. Applied Intelligence, 1–18.YangL. J.YanL. L.YangX. H.XinX.XueL. G. (2022). Bayesian nonnegative matrix factorization in an incremental manner for data representation. , 1–18.Search in Google Scholar
Yang, Q., Yin, S. H., Li, Q. P, & Li, Y. P. (2022). Analysis of electricity consumption behaviors based on principal component analysis and density peak clustering. Concurrency and Computation: Practice and Experience, 34(21), e7126.YangQ.YinS. H.LiQ. P.LiY. P. (2022). Analysis of electricity consumption behaviors based on principal component analysis and density peak clustering. , 34(21), e7126.Search in Google Scholar
Yao, Y Q., Meng, H., Gao, Y., Long, Z. G., & Li, T. R. (2023). Linear dimensionality reduction method based on topological properties. Information Sciences, 624, 493–511.YaoY. Q.MengH.GaoY.LongZ. G.LiT. R. (2023). Linear dimensionality reduction method based on topological properties. , 624, 493–511.Search in Google Scholar
Yunita, A., Santoso, H. B., & Hasibuan, Z. A. (2022). ‘Everything is data’: towards one big data ecosystem using multiple sources of data on higher education in Indonesia. Journal of Big Data, 9(1), 1–22.YunitaA.SantosoH. B.HasibuanZ. A. (2022). ‘Everything is data’: towards one big data ecosystem using multiple sources of data on higher education in Indonesia. , 9(1), 1–22.Search in Google Scholar
Zhang, N., Tian, Y., Wang, X. W., Xu, Y., Zhu, Q. X., & He, Y. L. (2023). Novel Bootstrap-Based Discriminant NPE Integrated With Orthogonal LPP for Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 72, 1–9.ZhangN.TianY.WangX. W.XuY.ZhuQ. X.HeY. L. (2023). Novel Bootstrap-Based Discriminant NPE Integrated With Orthogonal LPP for Fault Diagnosis. , 72, 1–9.Search in Google Scholar
Boutsidis, C., Zouzias, A., Mahoney, M. W., & Drineas, P. (2014). Randomized dimensionality reduction for k-means clustering. IEEE Transactions on Information Theory, 61(2), 1045–1062.BoutsidisC.ZouziasA.MahoneyM. W.DrineasP. (2014). Randomized dimensionality reduction for k-means clustering. , 61(2), 1045–1062.Search in Google Scholar
Maldonado, S., Carrizosa, E., & Weber, R. (2015). Kernel penalized k-means: A feature selection method based on kernel k-means. Information sciences, 322, 150–160.MaldonadoS.CarrizosaE.WeberR. (2015). Kernel penalized k-means: A feature selection method based on kernel k-means. , 322, 150–160.Search in Google Scholar