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Image blurring and sharpening inspired three-way clustering approach

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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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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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