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Searching of Potentially Anomalous Signals in Cosmic-Ray Particle Tracks Images Using Rough k-Means Clustering Combined with Eigendecomposition-Derived Embedding

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Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

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Abstract

Our work presents the application of the rough sets method in the field of astrophysics for the analysis of observational data recorded by the Cosmic Ray Extremely Distributed Observatory (CREDO) project infrastructure. CREDO research has produced huge datasets that are not well yet studied in terms of the information they contain, including specific anomalous observations, which are of particular interest to physicists and other scientists. From the pool of data available for analysis registered under CREDO infrastructure, containing approximately \(10^7\) of events, a set of \(10^4\) of samples was selected. We have applied eigendecomposition-derived embedding limiting data to 62 dimensions (95% of variance). We have adapted rough k-means algorithm for the purpose of anomalies detection task. We have validated our approach on various configurations of adaptable parameters of the proposed algorithm. The potential anomalies retrieved with the proposed algorithm have morphological features consistent with what a human expert would expect from anomalous signals in this case. The source codes and data of our experiments are available for download to make research reproducible.

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References

  1. Aartsen, M.G., et al.: The IceCube neutrino observatory: instrumentation and online systems. J. Instrum. 12(03), P03012 (2017)

    Article  Google Scholar 

  2. Afridi, M.K., Azam, N., Yao, J., Alanazi, E.: A three-way clustering approach for handling missing data using GTRS. Int. J. Approximate Reasoning 98, 11–24 (2018)

    Article  MathSciNet  Google Scholar 

  3. Allekotte, I., et al.: The surface detector system of the pierre auger observatory. Nucl. Instrum. Methods Phys. Res. Sect. A 586(3), 409–420 (2008)

    Article  Google Scholar 

  4. Avrorin, A., et al.: Baikal-GVD: status and prospects. In: EPJ Web of Conferences, vol. 191, p. 01006. EDP Sciences (2018)

    Google Scholar 

  5. Avrorin, A., et al.: Deep-underwater Cherenkov detector in lake Baikal. J. Exp. Theor. Phys. 134(4), 399–416 (2022)

    Article  Google Scholar 

  6. Bar, O., et al.: Zernike moment based classification of cosmic ray candidate hits from CMOs sensors. Sensors 21(22), 7718 (2021). https://doi.org/10.3390/s21227718. https://www.mdpi.com/1424-8220/21/22/7718

  7. Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutorials 16(1), 303–336 (2013)

    Article  Google Scholar 

  8. Bibrzycki, Ł., et al.: Towards a global cosmic ray sensor network: Credo detector as the first open-source mobile application enabling detection of penetrating radiation. Symmetry 12(11), 1802 (2020). https://doi.org/10.3390/sym12111802, https://www.mdpi.com/2073-8994/12/11/1802

  9. Cai, Z., Guan, X., Shao, P., Peng, Q., Sun, G.: A rough set theory based method for anomaly intrusion detection in computer network systems. Expert. Syst. 20(5), 251–259 (2003)

    Article  Google Scholar 

  10. Cataldi, G., et al.: The upgrade of the Pierre auger observatory with the scintillator surface detector. Proc. Sci. 395, 251 (2022). https://doi.org/10.22323/1.395.0251

    Article  Google Scholar 

  11. Chimphlee, W., Abdullah, A.H., Sap, M.N.M., Chimphlee, S., Srinoy, S.: Unsupervised clustering methods for identifying rare events in anomaly detection. Eng. Technol. 2, 1 (2005)

    Google Scholar 

  12. Chimphlee, W., Abdullah, A.H., Sap, M.N.M., Srinoy, S., Chimphlee, S.: Anomaly-based intrusion detection using fuzzy rough clustering. In: 2006 International Conference on Hybrid Information Technology, vol. 1, pp. 329–334. IEEE (2006)

    Google Scholar 

  13. Collaboration, P.A., et al.: The pierre auger cosmic ray observatory. Nucl. Instrum. Methods Phys. Res., Sect. A 798, 172–213 (2015)

    Article  Google Scholar 

  14. Forgey, E.: Cluster analysis of multivariate data: efficiency vs. interpretability of classification. Biometrics 21(3), 768–769 (1965)

    Google Scholar 

  15. Hachaj, T., Koptyra, K., Ogiela, M.R.: Eigenfaces-based steganography. Entropy 23(3) (2021). https://doi.org/10.3390/e23030273, https://www.mdpi.com/1099-4300/23/3/273

  16. Hachaj, T., Piekarczyk, M.: The practice of detecting potential cosmic rays using CMOs cameras: hardware and algorithms. Sensors 23(10), 4858 (2023). https://doi.org/10.3390/s23104858, https://www.mdpi.com/1424-8220/23/10/4858

  17. Homola, P., Beznosko, D., Bhatta, G., Bibrzycki, Ł., et al.: Cosmic-ray extremely distributed observatory. Symmetry 12(11), 1835 (2020). https://doi.org/10.3390/sym12111835, https://www.mdpi.com/2073-8994/12/11/1835

  18. Karbowiak, M., et al.: Small shower array for education purposes-the credo-maze project. Proc. Sci. 395, 199 (2021)

    Google Scholar 

  19. Li, Y., et al.: Classification of bgp anomalies using decision trees and fuzzy rough sets. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1312–1317 (2014). https://doi.org/10.1109/SMC.2014.6974096

  20. Lin, T.: Anomaly detection. In: Proceedings New Security Paradigms Workshop, pp. 44–53 (1994). https://doi.org/10.1109/NSPW.1994.656226

  21. Lin, T.: Anomaly detection. In: Proceedings New Security Paradigms Workshop, pp. 44–53. IEEE (1994)

    Google Scholar 

  22. Lingras, P., Peters, G.: Applying rough set concepts to clustering. In: Peters, G., Lingras, P., Slezak, D., Yao, Y. (eds.) Rough Sets: Selected Methods and Applications in Management and Engineering. Advanced Information and Knowledge Processing, pp. 23–37. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2760-4_2

    Chapter  Google Scholar 

  23. Liu, H., Zhou, J., Li, H.: Using rough sets to improve the high-dimensional data anomaly detection method based on extended isolation forest. In: 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 231–236. IEEE (2023)

    Google Scholar 

  24. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  Google Scholar 

  25. Mazarbhuiya, F.A.: Detecting anomaly using neighborhood rough set based classification approach (2022). Available at SSRN 4124453

    Google Scholar 

  26. Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021)

    Article  Google Scholar 

  27. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  Google Scholar 

  28. Peters, J.F., Skowron, A., Suraj, Z., Rzasa, W., Borkowski, M.: Clustering: a rough set approach to constructing information granules. In: Soft Computing and Distributed Processing, Proceedings of 6th International Conference, SCDP, vol. 5761 (2002)

    Google Scholar 

  29. Piekarczyk, M., Bar, O., Bibrzycki, Ł., Niedźwiecki, M., et al.: CNN-based classifier as an offline trigger for the CREDO experiment. Sensors 21(14), 4804 (2021). https://doi.org/10.3390/s21144804, https://www.mdpi.com/1424-8220/21/14/4804

  30. Pięta, P., Szmuc, T.: Applications of rough sets in big data analysis: an overview. Int. J. Appl. Math. Comput. Sci. 31(4), 659–683 (2021)

    MathSciNet  Google Scholar 

  31. Pryga, J., et al.: Analysis of the capability of detection of extensive air showers by simple scintillator detectors. Universe 8(8), 425 (2022)

    Article  Google Scholar 

  32. Rawat, S.S., Polavarapu, V.A., Kumar, V., Aruna, E., Sumathi, V.: Anomaly detection in smart grid using rough set theory and k cross validation. In: 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], pp. 479–483. IEEE (2014)

    Google Scholar 

  33. Riza, L.S., et al.: Implementing algorithms of rough set theory and fuzzy rough set theory in the r package “roughsets". Inf. Sci. 287, 68–89 (2014)

    Article  Google Scholar 

  34. Skowron, A., Dutta, S.: Rough sets: past, present, and future. Nat. Comput. 17, 855–876 (2018)

    Article  MathSciNet  Google Scholar 

  35. Skowron, A., Ślęzak, D.: Rough sets turn 40: From information systems to intelligent systems. In: 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), pp. 23–34. IEEE (2022)

    Google Scholar 

  36. Stasielak, J., et al.: High-energy neutrino astronomy-baikal-gvd neutrino telescope in lake baikal. Symmetry 13(3), 377 (2021)

    Article  Google Scholar 

  37. Taha, A., Hadi, A.S.: Anomaly detection methods for categorical data: a review. ACM Comput. Surv. (CSUR) 52(2), 1–35 (2019)

    Article  Google Scholar 

  38. Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991). https://doi.org/10.1109/CVPR.1991.139758

  39. Wang, P., Yao, Y.: CE3: a three-way clustering method based on mathematical morphology. Knowl.-Based Syst. 155, 54–65 (2018)

    Article  Google Scholar 

  40. Wei, R., Mahmood, A.: Recent advances in variational autoencoders with representation learning for biomedical informatics: a survey. IEEE Access 9, 4939–4956 (2021). https://doi.org/10.1109/ACCESS.2020.3048309

    Article  Google Scholar 

  41. Yuan, Z., Chen, B., Liu, J., Chen, H., Peng, D., Li, P.: Anomaly detection based on weighted fuzzy-rough density. Appl. Soft Comput. 134, 109995 (2023). https://doi.org/10.1016/j.asoc.2023.109995, https://www.sciencedirect.com/science/article/pii/S1568494623000133

  42. Zeng, F., Yin, K., Chen, M., Wang, X.: A new anomaly detection method based on rough set reduction and hmm. In: 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science, pp. 285–289 (2009). https://doi.org/10.1109/ICIS.2009.140

  43. Zeng, F., Yin, K., Chen, M., Wang, X.: A new anomaly detection method based on rough set reduction and hmm. In: 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science, pp. 285–289. IEEE (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Al. Mickiewicza 30, 30-059, Krakow, Poland

    Tomasz Hachaj, Marcin Piekarczyk & Jarosław Wąs

Authors
  1. Tomasz Hachaj
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  2. Marcin Piekarczyk
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  3. Jarosław Wąs
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Corresponding author

Correspondence to Tomasz Hachaj .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin Xia

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

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Hachaj, T., Piekarczyk, M., Wąs, J. (2023). Searching of Potentially Anomalous Signals in Cosmic-Ray Particle Tracks Images Using Rough k-Means Clustering Combined with Eigendecomposition-Derived Embedding. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_30

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  • DOI: https://doi.org/10.1007/978-3-031-50959-9_30

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