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Correlation-Driven Imputation for Heterogeneous Bibliographic Data: A Framework for Library Availability and Scopus Records
Authors
1 Research Scholar, Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, Tamil Nadu, India. 2 Associate Professor, Department of Computer Science with Cognitive Systems, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamil Nadu, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 791-797
- Article Type
- Original Article
- Check for updates
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References
1. R. J. A. Little and D. B. Rubin, Statistical Analysis with Missing Data, 3rd ed. Hoboken, NJ, USA: Wiley, 2019.
2. D. B. Rubin, Multiple Imputation for Nonresponse in Surveys. New York, NY, USA: Wiley, 1987.
3. W.-C. Lin and C.-F. Tsai, “Missing value imputation: A review and analysis of the literature (2006–2017),” Artificial Intelligence Review, vol. 53, pp. 1487–1509, 2020, doi: 10.1007/s10462-019-09709-4.
4. M. K. Hasan, M. A. Alam, S. Roy, A. Dutta, M. T. Jawad, and S. Das, “Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021),” Informatics in Medicine Unlocked, vol. 27, Art. no. 100799, 2021, doi: 10.1016/j.imu.2021.100799.
5. S. van Buuren and K. Groothuis-Oudshoorn, “mice: Multivariate imputation by chained equations in R,” Journal of Statistical Software, vol. 45, no. 3, pp. 1–67, 2011, doi: 10.18637/jss.v045.i03.
6. S. van Buuren, Flexible Imputation of Missing Data, 2nd ed. Boca Raton, FL, USA: CRC Press, 2018, doi: 10.1201/9780429492259.
7. D. J. Stekhoven and P. Bühlmann, “MissForest—non-parametric missing value imputation for mixed-type data,” Bioinformatics, vol. 28, no. 1, pp. 112–118, 2012, doi: 10.1093/bioinformatics/btr597.
8. R. Lall and T. Robinson, “The MIDAS touch: Accurate and scalable missing-data imputation with deep learning,” Political Analysis, vol. 30, no. 2, pp. 179–196, 2022, doi: 10.1017/pan.2020.49.
9. A. M. Sefidian and N. Daneshpour, “Estimating missing data using novel correlation maximization based methods,” Applied Soft Computing, vol. 91, Art. no. 106249, 2020, doi: 10.1016/j.asoc.2020.106249.
10. K. Phiwhorm, C. Saikaew, C. K. Leung, P. Polpinit, and K. R. Saikaew, “Adaptive multiple imputations of missing values using the class center,” Journal of Big Data, vol. 9, Art. no. 52, 2022, doi: 10.1186/s40537-022-00608-0.
11. J. L. Schafer, Analysis of Incomplete Multivariate Data. London, U.K.: Chapman & Hall, 1997.
12. P. D. Allison, Missing Data. Thousand Oaks, CA, USA: Sage Publications, 2001.
13. J. W. Graham, “Missing data analysis: Making it work in the real world,” Annual Review of Psychology, vol. 60, pp. 549–576, 2009, doi: 10.1146/annurev.psych.58.110405.085530.
14. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proc. 25th Int. Conf. Machine Learning (ICML), Helsinki, Finland, 2008, pp. 1096–1103, doi: 10.1145/1390156.1390294.
15. J. W. Graham, A. E. Olchowski, and T. D. Gilreath, “How many imputations are really needed? Some practical clarifications of multiple imputation theory,” Prevention Science, vol. 8, pp. 206–213, 2007, doi: 10.1007/s11121-007-0070-9.
16. S. van Buuren, “Multiple imputation of discrete and continuous data by fully conditional specification,” Statistical Methods in Medical Research, vol. 16, no. 3, pp. 219–242, 2007, doi: 10.1177/0962280206074463.
17. R. J. A. Little, “A test of missing completely at random for multivariate data with missing values,” Journal of the American Statistical Association, vol. 83, no. 404, pp. 1198–1202, 1988, doi: 10.1080/01621459.1988.10478722.
18. J. L. Schafer and J. W. Graham, “Missing data: Our view of the state of the art,” Psychological Methods, vol. 7, no. 2, pp. 147–177, 2002, doi: 10.1037/1082-989X.7.2.147
2. D. B. Rubin, Multiple Imputation for Nonresponse in Surveys. New York, NY, USA: Wiley, 1987.
3. W.-C. Lin and C.-F. Tsai, “Missing value imputation: A review and analysis of the literature (2006–2017),” Artificial Intelligence Review, vol. 53, pp. 1487–1509, 2020, doi: 10.1007/s10462-019-09709-4.
4. M. K. Hasan, M. A. Alam, S. Roy, A. Dutta, M. T. Jawad, and S. Das, “Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021),” Informatics in Medicine Unlocked, vol. 27, Art. no. 100799, 2021, doi: 10.1016/j.imu.2021.100799.
5. S. van Buuren and K. Groothuis-Oudshoorn, “mice: Multivariate imputation by chained equations in R,” Journal of Statistical Software, vol. 45, no. 3, pp. 1–67, 2011, doi: 10.18637/jss.v045.i03.
6. S. van Buuren, Flexible Imputation of Missing Data, 2nd ed. Boca Raton, FL, USA: CRC Press, 2018, doi: 10.1201/9780429492259.
7. D. J. Stekhoven and P. Bühlmann, “MissForest—non-parametric missing value imputation for mixed-type data,” Bioinformatics, vol. 28, no. 1, pp. 112–118, 2012, doi: 10.1093/bioinformatics/btr597.
8. R. Lall and T. Robinson, “The MIDAS touch: Accurate and scalable missing-data imputation with deep learning,” Political Analysis, vol. 30, no. 2, pp. 179–196, 2022, doi: 10.1017/pan.2020.49.
9. A. M. Sefidian and N. Daneshpour, “Estimating missing data using novel correlation maximization based methods,” Applied Soft Computing, vol. 91, Art. no. 106249, 2020, doi: 10.1016/j.asoc.2020.106249.
10. K. Phiwhorm, C. Saikaew, C. K. Leung, P. Polpinit, and K. R. Saikaew, “Adaptive multiple imputations of missing values using the class center,” Journal of Big Data, vol. 9, Art. no. 52, 2022, doi: 10.1186/s40537-022-00608-0.
11. J. L. Schafer, Analysis of Incomplete Multivariate Data. London, U.K.: Chapman & Hall, 1997.
12. P. D. Allison, Missing Data. Thousand Oaks, CA, USA: Sage Publications, 2001.
13. J. W. Graham, “Missing data analysis: Making it work in the real world,” Annual Review of Psychology, vol. 60, pp. 549–576, 2009, doi: 10.1146/annurev.psych.58.110405.085530.
14. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proc. 25th Int. Conf. Machine Learning (ICML), Helsinki, Finland, 2008, pp. 1096–1103, doi: 10.1145/1390156.1390294.
15. J. W. Graham, A. E. Olchowski, and T. D. Gilreath, “How many imputations are really needed? Some practical clarifications of multiple imputation theory,” Prevention Science, vol. 8, pp. 206–213, 2007, doi: 10.1007/s11121-007-0070-9.
16. S. van Buuren, “Multiple imputation of discrete and continuous data by fully conditional specification,” Statistical Methods in Medical Research, vol. 16, no. 3, pp. 219–242, 2007, doi: 10.1177/0962280206074463.
17. R. J. A. Little, “A test of missing completely at random for multivariate data with missing values,” Journal of the American Statistical Association, vol. 83, no. 404, pp. 1198–1202, 1988, doi: 10.1080/01621459.1988.10478722.
18. J. L. Schafer and J. W. Graham, “Missing data: Our view of the state of the art,” Psychological Methods, vol. 7, no. 2, pp. 147–177, 2002, doi: 10.1037/1082-989X.7.2.147
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