References
- Little, Roderick JA, and Donald B. Rubin, Statistical analysis with missing data, John Wiley & Sons, (2010).
- Schmitt, P., Mandel, J., & Guedj, MA, Comparison of six methods for missing data imputation: Journal of Biometrics & Biostatistics, 6(1), (2015).
- Beretta, L., & Santaniello, A., Nearest neighbor imputation algorithms: A critical evaluation: BMC medical informatics and decision making, 16(3), 74, (2016).
- Toutenburg, H., & Nittner, T., Linear regression models with incomplete categorical covariates:Computational Statistics, 17(2), 215-232, (2002).
- Hwang, S., Oh, J., Cox, J., Tang, S. J., & Tibbals, H. F., Blood detection in wireless capsule endoscopy using expectation maximization clustering, In Medical Imaging 2006:Image Processing (Vol. 6144, p. 61441P). International Society for Optics and Photonics, (2006).
- Hathaway, R. J., & Bezdek, J. C., Fuzzy c-means clustering of incomplete data:IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31(5), 735-744, (2001). doi: 10.1109/3477.956035
- Saravanan, P., & Sailakshmi, P., Missing value imputation using fuzzy Zpossibilistic c means optimized with support vector regression and genetic algorithm: Journal of Theoretical & Applied Information Technology, 72(1), (2015).
- Li, D., Gu, H., & Zhang, L., A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data. Expert Systems with Applications, 37(10), 6942-6947, (2010). doi: 10.1016/j.eswa.2010.03.028
- Zhang, Q., & Chen, Z., A distributed weighted possibilistic c-means algorithm for clustering incomplete big sensor data: International Journal of Distributed Sensor Networks, 10(5), 430814, (2014). doi: 10.1155/2014/430814
- Zhang, L., Lu, W., Liu, X., Pedrycz, W., & Zhong, C. Fuzzy c-means clustering of incomplete data based on probabilistic information granules of missing values: Knowledge-Based Systems, 99, 51-70, (2016). doi: 10.1016/j.knosys.2016.01.048
- Li, J., Song, S., Zhang, Y., & Zhou, Z., Robust k-median and k-means clustering algorithms for incomplete data: Mathematical Problems in Engineering, (2016).
- Noor, M. N., Yahaya, A. S., Ramli, N. A., & Al Bakri, A. M. M., Filling missing data using interpolation methods: study on the effect of fitting distribution (Vol. 594, pp. 889-895). Trans Tech Publications, (2014).
- White, I. R., Royston, P., & Wood, A. M., Multiple imputation using chained equations: issues and guidance for practice: Statistics in medicine, 30(4), 377-399, (2011). doi: 10.1002/sim.4067
- Zhao, X., Li, Y., & Zhao, Q., Mahalanobis distance based on fuzzy clustering algorithm for image segmentation: Digital Signal Processing, 43, 8-16, (2015). doi: 10.1016/j.dsp.2015.04.009
- Gueorguieva, N., Valova, I., & Georgiev, G., M&mfcm: fuzzy c-means clustering with mahalanobis and minkowski distance metrics: Procedia computer science, 114, 224-233, (2017). doi: 10.1016/j.procs.2017.09.064
- Bache, K., & Lichman, M. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml] Irvine, CA: University of California. School of information and computer science, 28, (2013).
- Huang, Z., & Ng, M. K., A fuzzy k-modes algorithm for clustering categorical data: IEEE Transactions on Fuzzy Systems, 7(4), 446-452, (1999). doi: 10.1109/91.784206
- Rand, W. M., Objective criteria for the evaluation of clustering methods: Journal of the American Statistical association, 66(336), 846-850, (1971). doi: 10.1080/01621459.1971.10482356
- Strehl, A., & Ghosh, J., Cluster ensembles–a knowledge reuse framework for combining multiple partitions: Journal of machine learning research, 3(Dec), 583-617, (2002).