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Image blurring and sharpening inspired three-way clustering approach

Applied Intelligence volume 52, pages 18131–18155 (2022)Cite this article

Abstract

Three-way clustering is a new type of clustering algorithm that divides the clustering results into three different parts or regions. This division allows a clear distinction between the central core and the outer sparse or fringe regions of a cluster. This algorithm is useful in situations when clusters have an unclear and unsharp boundary. In existing studies, a pair of thresholds are typically used to define the three regions of three-way clustering which demands the determination of suitable threshold values. In this paper, we propose an approach called blurring and sharpening based three-way clustering (BS3WC) which constructs the three-way clusters without the need for determining the thresholds. The BS3WC is motivated by observing that the blurring and sharpening operations can produce a three-way representation for a typical object in an image consisting of a core inner, outer blurry, and part not belonging to the object. The BS3WC works in two steps. In step one, it converts a hard cluster into an image. It next defines cluster blur and cluster sharp operations, which are used to create three-way representation for clusters. The BS3WC is validated with 31 datasets including both synthetic and real-life datasets using typical benchmarks of ACC, ARI, NMI and compared with the existing three-way as well as other notable approaches. We also consider the performance of the BS3WC approach in the application area of open-world classification for identifying unknown instances. Experimental results suggest that BS3WC may effectively cluster the data and provide results that are comparable to well-known approaches in the considered application area.

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References

  1. Afridi MK, Azam N, Yao JT (2020) Variance based three-way clustering approaches for handling overlapping clustering. International Journal of Approximate Reasoning 118:47–63

    Article  MathSciNet  MATH  Google Scholar 

  2. Afridi MK, Azam N, Yao JT, Alanazi E (2018) A three-way clustering approach for handling missing data using GTRS. International Journal of Approximate Reasoning 98:11–24

    Article  MathSciNet  MATH  Google Scholar 

  3. Ali B, Azam N, Shah A, Yao JT (2021) A spatial filtering inspired three-way clustering approach with application to outlier detection. International Journal of Approximate Reasoning 130:1–21

    Article  MathSciNet  MATH  Google Scholar 

  4. Bache K, Lichman M (2013) UCI machine learning repository http://archive.ics.uci.edu/ml (retrieved: 2021-01-02)

  5. Bendale A, Boult T (2015) Towards open world recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1893–1902

  6. Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Computers and Geosciences 10(2–3):191–203

    Article  Google Scholar 

  7. Campagner A, Ciucci D (2019) Orthopartitions and soft clustering: soft mutual information measures for clustering validation. Knowledge-Based Systems 180:51–61

    Article  Google Scholar 

  8. Chen M, Miao D (2011) Interval set clustering. Expert Systems with Applications 38(4):2923–2932

    Article  Google Scholar 

  9. Chu X, Sun B, Li X, Han K, Wu J, Zhang Y, Huang Q (2020) Neighborhood rough set-based three-way clustering considering attribute correlations: an approach to classification of potential gout groups. Information Sciences 535:28–41

    Article  MathSciNet  Google Scholar 

  10. Fei G, Liu B (2016) Breaking the closed world assumption in text classification. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies pp 506–514

  11. Fränti P, Sieranoja S (2018) clustering datasets, http://cs.joensuu.fi/sipu/datasets/ (retrieved: 2020-11-22) (2018)

  12. Ge Z, Demyanov S, Chen Z, Garnavi R (2017) Generative openmax for multi-class open set classification. In: Proceedings of the British Machine Vision Conference - Royal Geographic Society of London

  13. Geng G, Huang SJ, Chen S (2020) Recent advances in open set recognition: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(10):3614–3631

    Article  Google Scholar 

  14. Gonzalez RC, Wintz P (2018) Digital image processing, fourth edition. Addison-Wesley Publishing

  15. Júnior PRM, DeSouza RM, Werneck RDO, Stein BV, Pazinato DV, Almeida WRD, Penatti OA, Torres RDS, Rocha A (2017) Nearest neighbors distance ratio open-set classifier. Machine Learning 106(3):359–386

    Article  MathSciNet  MATH  Google Scholar 

  16. Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Information sciences 378:244–263

    Article  MATH  Google Scholar 

  17. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering Algorithm. Pattern Recognition 36(2):451–461

    Article  Google Scholar 

  18. Lingras P, West C (2004) Interval set clustering of web users with rough k-means. Journal of Intelligent Information Systems 23(1):5–16

    Article  MATH  Google Scholar 

  19. McInnes L, Healy J, Astels S (2017) hdbscan: Hierarchical density based clustering. Journal of Open Source Software 2(11):205

    Article  Google Scholar 

  20. Mundt M, Pliushch I, Majumder S, Ramesh V (2019) Open set recognition through deep neural network uncertainty: Does out-of-distribution detection require generative classifiers? In: Proceedings of International Conference on Computer Vision Workshops pp 753–757

  21. Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: Analysis and an algorithm. In: Proceedings of the Conference on Advances in Neural Information Processing Systems. pp 849–856

  22. Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(1):103–109

    Article  Google Scholar 

  23. Perera P, Morariu VI, Jain R, Manjunatha V, Wigington C, Ordonez V, Patel VM (2020) Generative-discriminative feature representations for open-set recognition. In: Proceedings of the Computer Vision and Pattern Recognition. pp 11814–11823

  24. Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  25. Scheirer WJ, Rocha ADR, Sapkota A, Boult TE (2012) Toward open set recognition. IEEE transactions on pattern analysis and machine intelligence 35(7):1757–1772

    Article  Google Scholar 

  26. Shu L, Xu H, Liu B (2017) Doc: Deep open classification of text documents. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing pp 2911–2916

  27. Wang P, Liu Q, Xu G, Wang K (2019) A three-way clustering method based on ensemble strategy and three-way decision. Information 10(2):59

    Article  Google Scholar 

  28. Wang P, Shi H, Yang X, Mi J (2019) Three-way kmeans: integrating k-means and three-way decision. International Journal of Machine Learning and Cybernetics 10(10):2767–2777

    Article  Google Scholar 

  29. Wang P, Yao YY (2018) Ce3: A three-way clustering method based on mathematical morphology. Knowledge-Based Systems 155:54–65

    Article  Google Scholar 

  30. Xiong J, Yu H (2018) An adaptive three-way clustering algorithm for mixed-type data. Proceedings of the International Symposium on Methodologies for Intelligent Systems, Lecture Notes in Computer Science 12117:379–388

    Google Scholar 

  31. Yang Y, Hou C, Lang Y, Guan D, Huang D, Xu J (2019) Open-set human activity recognition based on micro-doppler signatures. Pattern Recognition 85:60–69

    Article  Google Scholar 

  32. Yao YY (2019) Tri-level thinking: models of three-way decision. International Journal of Machine Learning and Cybernetics pp 1–13

  33. Yao YY (2011) The superiority of three-way decisions in probabilistic rough set models. Information Sciences 181(6):1080–1096

    Article  MathSciNet  MATH  Google Scholar 

  34. Yao YY (2012) An outline of a theory of three-way decisions. Proceedings of the International Conference on Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science 7413:1–17

    MATH  Google Scholar 

  35. Yao YY (2015) Rough sets and three-way decisions. Proceedings of the International Conference on Rough Sets and Knowledge Technology, Lecture Notes in Computer Science 9436:62–73

    MATH  Google Scholar 

  36. Yao YY (2018) Three-way decision and granular computing. International Journal of Approximate Reasoning 103:107–123

    Article  MATH  Google Scholar 

  37. Yao YY (2021) Set-theoretic models of three-way decision. Granular Computing 6(1):133–148

    Article  Google Scholar 

  38. Yu H (2017) A framework of three-way cluster analysis. Proceedings of the International Joint Conference on Rough Sets, Lecture Notes in Computer Science 10314:300–312

    Google Scholar 

  39. Yu H, Chang Z, Wang G, Chen X (2020) An efficient three-way clustering algorithm based on gravitational search. International Journal of Machine Learning and Cybernetics 11(5):1003–1016

    Article  Google Scholar 

  40. Yu H, Chang Z, Zhou B (2017) A novel three-way clustering algorithm for mixed-type data. In: Proceedings of International Conference on Big Knowledge. pp 119–126

  41. Yu H, Chen Y, Lingras P, Wang G (2019) A three-way cluster ensemble approach for large-scale data. International Journal of Approximate Reasoning 115:32–49

    Article  MathSciNet  MATH  Google Scholar 

  42. Yu H, Liu Z, Wang G (2014) An automatic method to determine the number of clusters using decision-theoretic rough set. International Journal of Approximate Reasoning 55(1):101–115

    Article  MathSciNet  MATH  Google Scholar 

  43. Yu H, Su T, Zeng X (2014) A three-way decisions clustering algorithm for incomplete data. Proceedings of the International Conference on Rough Sets and Knowledge Technology, Lecture Notes in Computer Science, Lecture Notes in Computer Science 8818:765–776

    MATH  Google Scholar 

  44. Yu H, Wang X, Wang G (2017) A semi-supervised three-way clustering framework for multi-view data. Proceedings of the International Joint Conference on Rough Sets, Lecture Notes in Computer Science 10314:313–325

    Google Scholar 

  45. Yu H, Wang X, Wang G, Zeng X (2020) An active three-way clustering method via low-rank matrices for multi-view data. Information Sciences 507:823–839

    Article  Google Scholar 

  46. Yu H, Zhang C, Wang G (2016) A tree-based incremental overlapping clustering method using the three-way decision theory. Knowledge-Based Systems 91:189–203

    Article  Google Scholar 

  47. Yu H, Chen L, Yao JT (2020) A three-way density peak clustering method based on evidence theory. Knowledge-Based Systems, pp 106532

  48. Yu H, Chen L, Yao JT, Wang X (2019) A three-way clustering method based on an improved dbscan algorithm. Physica A: Statistical Mechanics and its Applications 535:122289

    Article  Google Scholar 

  49. Zhang C, Gao R, Qin H, Feng X (2021) Three-way clustering method for incomplete information system based on set-pair analysis. Granular Computing, pp 1–10

  50. Zhang H, Patel VM (2016) Sparse representation-based open set recognition. IEEE transactions on pattern analysis and machine intelligence 39(8):1690–1696

    Article  Google Scholar 

  51. Zhang Y, Miao D, Wang J, Zhang Z (2019) A cost-sensitive three-way combination technique for ensemble learning in sentiment classification. International Journal of Approximate Reasoning 105:85–97

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was partially supported by faculty research support grant of NUCES, Pakistan, NSERC discovery grant Canada, and the Deanship of Scientific Research at Umm Al-Qura University under Grant 19-COM-1-01-0023.

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Peshawar, Pakistan

    Anwar Shah & Nouman Azam

  2. Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia

    Eisa Alanazi

  3. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

    JingTao Yao

Authors
  1. Anwar Shah
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  2. Nouman Azam
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  3. Eisa Alanazi
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  4. JingTao Yao
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Contributions

Anwar Shah: Conceptualization, Investigation, Methodology, Software, Validation, Writing - Original Draft Nouman Azam: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing - Original Draft, Writing - Review & Editing Eisa Alanazi: Funding acquisition, Supervision, Writing - Review & Editing JingTao Yao: Funding acquisition, Methodology, Supervision, Writing - Review & Editing

Corresponding author

Correspondence to Nouman Azam.

Appendices

Appendices

2D Intensified Image of 4 Synthetic Datasets.

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Shah, A., Azam, N., Alanazi, E. et al. Image blurring and sharpening inspired three-way clustering approach. Appl Intell 52, 18131–18155 (2022). https://doi.org/10.1007/s10489-021-03072-0

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