[1] X. V. Wu, "The top ten algorithm in data mining," International Standard Book, vol. 13, pp. 978-1, 2009.
[2] E. J. J. L. Fix, "Discriminatory analysis-nonparametric discrimination: consistency properties," California Univ Berkeley, Texas, 1951.
[3] T. P. Cover, "Nearest neighbor pattern classification," IEEE transactions on information theory, vol. 13, no. 1, pp. 21-27, 1967.
[4] A. N. Y. Papadopoulos, Nearest Neighbor Search: A Database Perspective, New York: Springer Science & Business Media, 2006.
[5] D. T. Larose, Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, 2014.
[6] K. G. Q. L. J. Z. J. X. W. M. Zheng, "Applications of support vector machine and improved k-Nearest Neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear," in 2017 1st International Conference on Electrical Materials and Power Equipment, Xian, PEOPLES R CHINA, 2017.
[7] M. P.-N. Steinbach, "kNN: k-nearest neighbors," in The top ten algorithms in data mining, Chapman and Hall/CRC, 2009, pp. 165-176..
[8] Y. Lin, J. Li, M. Lin and J. Chen, "A new nearest neighbor classifier via fusing neighborhood information," Neurocomputing, pp. 164-169, 2014.
[9] S. V. V. Boriah, "Similarity measures for categorical data: A comparative evaluation," in Proceedings of the 2008 SIAM international conference on data mining, 2008.
[10] Z. H. Šulc, "Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering," Journal of Classification, vol. 36, no. 1, pp. 58-72, 2019.
[11] S. D. Z. Z. Luo, "Non-Numerical Nearest Neighbor Classifiers with Value-Object Hierarchical Embedding," Expert Systems with Applications, vol. 150, p. 113206, 2020.
[12] A. H. V. Desai, "Disc: Data-intensive similarity measure for categorical data," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin, Heidelberg, 2011.
[13] L. Y. G. J. Chen, "Kernel-based linear classification on categorical data," Soft Computing, vol. 20, no. 8, pp. 2981-2993, 2016.
[14] D. Z. Z. Luo, "Non-Numerical Nearest Neighbor Classifiers with Value-Object Hierarchical Embedding," Expert Systems with Applications, p. 113206, 2020.
[15] L. G. Chen, "Nearest neighbor classification of categorical data by attributes weighting," Expert Systems with Applications, vol. 42, no. 6, pp. 3142-3149, 2015.
[16] J. P. G. C. Domeniconi, "Locally adaptive metric nearest-neighbor classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1281-1285, 2002.
[17] M. S. A. K. M. R. M. K. R. M. K. S. Huda and C. M. Rahman, "A dynamic k-nearest neighbor algorithm for pattern analysis problem," in 3rd International Conference on Electrical & Computer Engineering, 2004.
[18] J. Hocke and T. Martinetz, "Feature weighting by maximum distance minimization," in International Conference on Artificial Neural Networks, 2013.
[19] M. R. H. M. M. B. J. R. K. Hassan, "Improving k-nearest neighbour classification with distance functions based on receiver operating characteristics," in Machine Learning and Knowledge Discovery in Databases, Berlin, Heidelberg, 2008.
[20] K. U. E. B. O. S. Syaliman, "Improving the accuracy of k-nearest neighbor using local mean based and distance weight," in 2nd International Conference on Computing and Applied Informatics 2017, ICCAI 2017, Medan, INDONESIA, 2018.
[21] G. D. Tutz, "Improved nearest neighbor classifiers by weighting and selection of predictors," Statistics and Computing, vol. 26, no. 5, pp. 1039-1057, 2016.
[22] J. H. W. S. Y. H. Gou, "A generalized mean distance-based k-nearest neighbor classifier," Expert Systems with Applications, vol. 115, pp. 356-372, 2019.
[23] D. Delen, Real-world data mining: applied business analytics and decision making, Upper Saddle River, New Jersey: FT Press, 2014.
[24] A. Fargus, Optimisation of Correlation Matrix Memory Prognostic and Diagnostic Systems, University of York, 2015.
[25] P. Zerzucha and B. Walczak, "Concept of (dis) similarity in data analysis," TrAC Trends in Analytical Chemistry, vol. 38, pp. 116-128, 2012.
[26] J. Han, J. Pei and M. Kamber, Data mining: concepts and techniques, Elsevier, 2011
[27] T. J. T. R. J. J. H. Hastie, The elements of statistical learning: data mining, inference, and prediction, Second Edition ed., springer, 2011.
[28] R. J. Eberhart, "A new optimizer using particle swarm theory," in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya,Japan, 1995.
[29] M. R. N. E. A. Imran, "An Overview of Particle Swarm Optimization Variants," Procedia Engineering, pp. 491-496, 2013.
[30] K. Kameyama, "Particle Swarm Optimization - A Survey," IEICE Transactions on Information and Systems, pp. 1354-1361, 2009.
[31] W. F. A. A.-S. M. A. Abd-El-Wahed, "Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems," Journal of Computational and Applied Mathematics, vol. 236, no. 5, pp. 1446-1453, 2011.
[32] A. S. K. H. C. Ratnaweera, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,"IEEE Transactions on evolutionary computation, pp. 240-255, 2004.
[33] F. T. Provost, Data Science for Business: What you need to know about data mining and data-analytic thinking, United States of America: O’Reilly Media, Inc., 2013.
[34] N. L. A. S. Z. Z. N. E. A. Ghani, "Accuracy Assessment of Urban Growth Pattern Classification Methods Using Confusion Matrix and ROC Analysis," in International Conference on Soft Computing in Data Science, Singapore, 2015.