A Quality Index for Detection of Atypical Elements (Outliers)
References
- Aggarwal, C.C. (2013). Outlier Analysis, Springer, Cham.
- Agresti, A. (2002). Categorical Data Analysis, Wiley, Hoboken.
- Baszczyńska, A. (2016). Smoothing Parameter of the Density Functions for Random Variables in Economic Research, Lodz University Press, Łódź, (in Polish).
- Batool, F. and Hennig, C. (2021). Clustering with the average silhouette width, Computational Statistics and Data Analysis 158(6): 107190.
- Cateni, S., Colla, V. and Vannucci, M. (2008). Outlier detection methods for industrial applications, in J. Aramburo and A.R. Trevino (Eds), Advances in Robotics, Automation and Control, I-Tech, Vienna, pp. 265–282.
- Caltech (2024). NASA Exoplanet Archive, https://exoplanetarchive.ipac.caltech.edu/.
- Chacon, J.E. and Duong, T. (2020). Multivariate Kernel Smoothing and Its Applications, Chapman and Hall/CRC, Boca Raton.
- Charytanowicz, M., Kulczycki, P., Kowalski, P.A., Lukasik, S. and Czabak-Garbacz, R. (2018). An evaluation of utilizing geometric features for wheat grain classification using x-ray images, Computers and Electronics in Agriculture 144(1): 260–268.
- Charytanowicz, M., Perzanowski, K., Januszczak, M., Wołoszyn-Gałęza, A. and Kulczycki, P. (2020). Application of complete gradient clustering algorithm for analysis of wildlife spatial distribution, Ecological Indicators 113(6): 106216.
- Czmil, S., Kluska, J. and Czmil, A. (2024). An empirical study of a simple incremental classifier based on vector quantizzation and adaptive resonance theory, International Journal of Applied Mathematics and Computer Science 34(1): 149–165, DOI: 10.61822/amcs-2024-0011.
- Dalianis, H. (2018). Clinical Text Mining, Springer, Cham.
- Hodge, V. (2011). Outlier and Anomaly Detection: A Survey of Outlier and Anomaly Detection Methods, Lambert Academic Publishing, Saarbrucken.
- James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J. (2023). An Introduction to Statistical Learning, Springer, Cham.
- Kacprzyk, J. and Pedrycz, W. (2015). Springer Handbook of Computational Intelligence, Springer, Berlin.
- Kaggle (2024). Suicide rates overview 1985 to 2016, Dataset, http://www.kaggle.com/datasets/russellyates88/suicide-rates-overview-1985-to-2016.
- Kłopotek, R., Kłopotek, M. and Wierzchoń, S. (2020). A feasible k-means kernel trick under non-Euclidean feature space, International Journal of Applied Mathematics and Computer Science 30(4): 703–715, DOI: 10.34768/amcs-2020-0052.
- Knuth, D.E. (1988). Art of Computer Programming. Vol. 3: Sorting and Searching, Addison-Wesley, Upper Saddle River.
- Kulczycki, P. (2005). Kernel Estimators in Systems Analysis, Scientific and Engineering Publishers,Warsaw, (in Polish).
- Kulczycki, P. (2020). Methodically unified procedures for outlier detection, clustering and classification, in K. Arai (Ed.), Proceedings of the Future Technologies Conference (FTC), Springer, Cham, pp. 460–474.
- Kulczycki, P. and Franus, K. (2021). Methodically unified procedures for a conditional approach to outlier detection, clustering, and classification, Information Sciences 560: 504–527.
- Kulczycki, P. and Kruszewski, D. (2017). Identification of atypical elements by transforming task to supervised form with fuzzy and intuitionistic fuzzy evaluations, Applied Soft Computing 60(11): 623–633.
- Kulczycki, P. and Kruszewski, D. (2019). Detection of rare elements in investigation of medical problems, in N.T. Nguen et al., (Eds), Intelligent Information and Database Systems, Springer, Singapore, pp. 257–268.
- Lehmann, E.L. and Casella, G. (2011). Theory of Point Estimation, Springer, New York.
- Nisbet, R., Miner, G. and Yale, K. (2009). Handbook of Statistical Analysis and Data Mining Applications, Elsevier, London.
- Ott, R.L. and Longnecker, M.T. (2015). An Introduction to Statistical Methods and Data Analysis, Cengage, Boston.
- Pedrycz, W. and Chen, S.-M. (2017). Data Science and Big Data: An Environment of Computational Intelligence, Springer, Cham.
- Rajagopalan, B. and Lall, U. (1995). A kernel estimator for discrete distributions, Journal of Nonparametric Statistics 4(1): 409–426.
- Ranga Suri, N.N.R., Narasimha-Murty, M. and Athithan, G. (2019). Outlier Detection: Techniques and Applications, Springer, Cham.
- scikit-learn (2004). make_circles, Dataset, https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html.
- Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, Chapman and Hall, London.
- Sorzano, C., Vargas, J. and Pascual-Montano, A. (2014). A survey of dimensionality reduction techniques, arXiv: 1403.2877v1.
- Wand, M.P. and Jones, M.C. (1995). Kernel Smoothing, Chapman and Hall, New York.
- Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference, Springer, New York.
- Yang, J., Tan, X. and Rahardja, S. (2023). Outlier detection: How to select k for k-nearest-neighbors-based outlier detectors, Pattern Recognition Letter 174: 112–117.
Language: English
Page range: 439 - 451
Submitted on: Feb 3, 2024
Accepted on: Jun 26, 2024
Published on: Oct 1, 2024
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Related subjects:
© 2024 Piotr Kulczycki, Krystian Franus, Małgorzata Charytanowicz, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.