A conditionally positive definite kernel function for clustering of incomplete data
*Sonia GoelCorresponding authorsoniagoel@msit.inDepartment of Electrical and Electronics EngineeringMaharaja Surajmal Institute of TechnologyGuru Gobind Singh Indraprastha UniversityNew Delhi, IndiaView full profile → , Meena Tushirmeenatushir@yahoo.comDepartment of Electrical and Electronics EngineeringMaharaja Surajmal Institute of TechnologyGuru Gobind Singh Indraprastha UniversityNew Delhi, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 17 Mar 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1557
- Pages:
- 403–412
Abstract
Keywords
Subject Classifications
References
[1] Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., & Franco, L. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine, 50(2), 105-115 (2010).
[2] 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).
[3] García-Laencina, P. J., Sancho-Gómez, J. L., Figueiras-Vidal, A. R., & Verleysen, M., K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing, 72(7-9), 1483-1493 (2009).
[4] Little, R.J. and Rubin, D.B., Statistical Analysis with Missing Data. 793, John Wiley & Sons, Hoboken (2019).
[5] Enders,C. K.,& Baraldi, A. N., Missing data handling methods. The Wiley handbook of psychometric testing: A multidisciplinary reference on survey, scale and test development, 139-185 (2018).
[6] Farhangfar,A.,Kurgan, L.A.,& Pedrycz, W., A novel framework for imputation of missing values in databases. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37(5), 692-709 (2007).
[7] Weed, L., Lok, R., Chawra, D.,& Zeitzer, J., The impact of missing data and imputation methods on the analysis of 24-hour activity patterns. Clocks & Sleep, 4(4), 497-507 (2022).
[8] Zhang, S., Nearest neighbor selection for iteratively kNN imputation. Journal of Systems and Software, 85(11), 2541-2552 (2012).
[9] Noor, N. M., Al Bakri Abdullah, M. M., Yahaya, A. S., & Ramli, N. A., Comparison of linear interpolation method and mean method to replace the missing values in environmental data set. In Materials Science Forum, 803, 278-281 (2015, January) Trans Tech Publications Ltd.
[10] Goel, S., & Tushir, M., Different approaches for missing data handling in fuzzy clustering: a review. Recent Advances in Electrical & Electronic Engineering, 13(6), 833-846 (2020).
[11] 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, 6144, p. 61441P. International Society for Optics and Photonics (2016).
[12] K. R. Muller, S. Mika, G. Ratsch and K. Tsuda, An introduction to kernel-based learning algorithms, IEEE transactions on neural networks, 12(2), 181-201 (2001).
[13] M. Girolami, “Mercer kernel-based clustering in feature space,” IEEE transactions on Neural Networks,13(3), 780-784 (2002).
[14] M. Tushir and S. Srivastava, A new kernel based hybrid c-means clustering model, In 2007 IEEE International Fuzzy Systems Conference, 1-5 (2007).
[15] Nigam, J., Tushir, M., & Rai, D., A conditionally positive definite kernel functions for possibilistic clustering. International Journal of Artificial Intelligence and Soft Computing, 6(1), 75-91 (2017).
[16] K. Bache and M. Lichman, UCI Machine Learning Repository [http://archive. ics. uci.edu/ml]. Irvine, CA: University of California, “School of information and computer science (2013).
