1. Caron M, Bojanowski P, Joulin A, Douze M (2018) Deep clustering for unsupervised learning of visual features. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany, September 2018. Springer, Cham, pp 132–149

2. Zhang H, Basu S, Davidson I (2019) Deep constrained clustering-algorithms and advances. ArXiv preprintarXiv:190110061

3. Gansbeke WV, Vandenhende S, Georgoulis S, Proesmans M, Gool LV (2020) Learning to classify images without labels. 2005.12320

4. Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A (2020) Unsupervised learning of visual features by contrasting cluster assignments. 2006.09882

5. Kim D, Lee B, Lee HJ, Lee SP, Moon Y, Jeong MK (2012) A graph kernel approach for detecting core patents and patent groups. IEEE Intell Syst 29(4):44–51

   Article  Google Scholar 

6. Fang Y, Gui-fa T (2015) Visual music score detection with unsupervised feature learning method based on K-means. Int J Mach Learn Cybern 6(2):277–287

   Article  Google Scholar 

7. Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms (SODA), Society for Industrial and Applied Mathematics, New Orleans, LA, USA, January 2007, pp 1027–1035

8. Gulnashin F, Sharma I, Sharma H (2019) A new deterministic method of initializing spherical K-means for document clustering. In: Progress in advanced computing and intelligent engineering. Springer, Singapore, pp 149–155

   Chapter  Google Scholar 

9. Jain A, Sharma I (2018) Clustering of text streams via facility location and spherical K-means. In: 2018 second international conference on electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, March 2018. IEEE, pp 1209–1213

10. Hartigan JA, Wong MA (1979) Algorithm as 136: a K-means clustering algorithm. J R Stat Soc Ser C (Applied Statistics) 28(1):100–108

    MATH  Google Scholar 

11. Sarma TH, Viswanath P, Reddy BE (2013) A hybrid approach to speed-up the K-means clustering method. Int J Mach Learn Cybern 4(2):107–117

    Article  Google Scholar 

12. Wang P, Shi H, Yang X, Mi J (2019) Three-way K-means: integrating K-means and three-way decision. Int J Mach Learn Cybern 10(10):2767–2777

    Article  Google Scholar 

13. Chen L, Xu Z, Wang H, Liu S (2018) An ordered clustering algorithm based on K-means and the promethee method. Int J Mach Learn Cybern 9(6):917–926

    Article  Google Scholar 

14. Cai Z, Zhou C, Li X (2019) Application research of employment recommendation based on improved K-means++ algorithm in colleges and universities. Appl Intell Syst Multimodal Inf Anal.https://doi.org/10.1007/978-3-030-15740-1_124

    Article  Google Scholar 

15. Solak S, Altinisik U (2018) A new method for classifying nuts using image processing and K-means++ clustering. J Food Process Eng 41(7):e12859

    Article  Google Scholar 

16. Maggioni M, Murphy JM (2019) Learning by unsupervised nonlinear diffusion. J Mach Learn Res 20(160):1–56.http://jmlr.org/papers/v20/18-873.html

17. Little A, Byrd A (2015) A multiscale spectral method for learning number of clusters. In: 2015 IEEE 14th international conference on machine learning and applications (ICMLA), Miami, FL, USA, December 2015. pp 457–460

18. Pelleg D, Moore AW (2000) X-means: extending K-means with efficient estimation of the number of clusters. In: Proceedings of the seventeenth international conference on machine learning (ICML), Stanford, CA, USA, June–July 2000. Morgan Kaufmann Publishers Inc., pp 727–734

19. Thomaz CE (2006) Fei face database.https://fei.edu.br/~cet/facedatabase.html. Accessed 1 Aug 2019

20. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720

    Article  Google Scholar 

21. Weyrauch B, Heisele B, Huang J, Blanz V (2004) Component-based face recognition with 3d morphable models. In: 2004 conference on computer vision and pattern recognition workshop (CVPR), Washington, DC, USA, June–July 2004. IEEE

22. Nefian AV (1999) Georgia tech face database.http://www.anefian.com/research/face_reco.htm. Accessed 1 Aug 2019

23. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE workshop on applications of computer vision (ACV), Sarasota, FL, USA, December 1994. IEEE, pp 138–142,https://git-disl.github.io/GTDLBench/datasets/att_face_dataset/

24. Computational Visions Group (1999) Faces 1999.http://www.vision.caltech.edu/html-files/archive.html. Accessed 1 Aug 2019

25. Fastai (2019) Imagenette: ImageNet Subset.https://github.com/fastai/imagenette. Accessed 1 May 2020

26. Blishen B, Carroll W, Moore C (2001) Prestige: Prestige of Canadian Occupations

27. Han EH, Boley D, Gini M, Gross R, Hastings K, Karypis G, Kumar V, Mobasher B, Moore J (1998) Webace: a web agent for document categorization and exploration. In: Proceedings of the second international conference on autonomous agents. ACM, pp 408–415

28. Greene D, Cunningham P (2006) Practical solutions to the problem of diagonal dominance in kernel document clustering. In: Proceedings of the 23rd international conference on Machine learning (ICML), Pittsburgh, PA, USA, June 2006. ACM, pp 377–384

29. Hubert L, Arabie P (1985) Comparing partitions. J Classif 2(1):193–218

    Article  Google Scholar 

30. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, USA, June 2015. IEEE, pp 815–823

31. Taniai H (2018) keras-facenet.https://github.com/nyoki-mtl/keras-facenet. Accessed 1 Aug 2019

32. Guo Y, Zhang L, Hu Y, He X, Gao J (2016) Ms-celeb-1m: a dataset and benchmark for large-scale face recognition. In: European conference on computer vision (ECCV), Amsterdam, The Netherlands, October 2016. Springer, Cham, pp 87–102

33. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, June–July 2016. IEEE, pp 770–778

34. Paszke A, Gross S, Massa F, Lerer A, Bradbury J,Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: An imperative style, high-performance deep learning library. In: Advances in neural information processing systems (NIPS) 32, Vancouver, Canada, December 2019. Curran Associates, Inc., pp 8024–8035

35. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems.https://www.tensorflow.org/, software available from tensorflow.org. Accessed 1 Aug 2019

36. Van Der Walt S, Colbert SC, Varoquaux G (2011) The numpy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22

    Article  Google Scholar 

37. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12(Oct):2825–2830

    MathSciNet MATH  Google Scholar 

38. Novikov A (2019) Pyclustering: data mining library. J Open Sour Softw 4(36):1230

    Article  Google Scholar 

39. Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple Valued Logic Soft Comput 17(3):255–287

    Google Scholar 

40. Ester M, Kriegel HP, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96:226–231

    Google Scholar 

41. Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) Optics: ordering points to identify the clustering structure. ACM Sigmod record. ACM 28:49–60

    Google Scholar 

Download references