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Various local clustering machine learning algorithms have been employed to recognize wafer map defect patterns:
         i) Fuzzy C means with the HCM [1].
         ii) Similarity-based clustering [2].
         iii) Infinite warped mixture model [3].
         iv) Density-based spatial clustering [4].

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[1] C.-H. Wang, S.-J. Wang, and W.-D. Lee, “Automatic identification of spatial defect patterns for semiconductor manufacturing,” International journal of production research, vol. 44, no. 23, pp. 5169–5185, 2006.
[2] T. Yuan, W. Kuo, and S. J. Bae, “Detection of spatial defect patterns generated in semiconductor fabrication processes,” IEEE Trans. Semicond. Manuf., vol. 24, no. 3, pp. 392–403, 2011.
[3] J. Kim, Y. Lee, and H. Kim, “Detection and clustering of mixed-type defect patterns in wafer bin maps,” Iise Transactions, vol. 50, no. 2, pp. 99–111, 2018.
[4] C. H. Jin, H. J. Na, M. Piao, G. Pok, and K. H. Ryu, “A novel dbscan-based defect pattern detection and classification framework for wafer bin map,” IEEE Trans. Semicond. Manuf., vol. 32, no. 3, pp. 286–292, 2019.