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EnSpeciVAT: Enhanced SpecieVAT for Cluster Tendency Identification in Graphs

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14178))

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Abstract

Clustering is a process of finding groups of similar objects in a given dataset. Finding clusters in graphs, especially crisp clusters, which have minimal or no overlapping clusters, is challenging. Further, clustering is an ill-defined problem, resulting in multiple possible solutions for the same dataset. Hence, a challenge here is that the possible number of crisp clusters that can be found for a given graph will not be unique. Finding different crisp clusters is useful for modelling patterns or cluster-based prediction tasks. The visual assessment of the clustering tendency (VAT) algorithm, in particular the SpecieVAT algorithm, has been used in the past to identify the number of clusters, i.e., the cluster tendencies, that exist in a graph. These algorithms generate an image of the reordered dissimilarity matrix, and the dark diagonal blocks in the main diagonal reveal the number of clusters that exist in the data. However, this method often fails to show the possible crisp clusters in the graph. We propose a novel algorithm, called EnSpeciVAT, which significantly enhances the SpecVAT algorithm in the context of crisp cluster generation for graph data. It incorporates a fuzzy c-means mechanism to guide the process of extracting crisp clusters in the graph. Our evaluation of six different graph data demonstrates that the EnSpeciVAT can find clear and crisp clusters in graph data.

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Acknowledgements

This research was funded by the Australian Research Council. ARC - Discovery Project, DP200101960.

Author information

Authors and Affiliations

  1. Deakin University, Geelong, Australia

    Siqi Xia, Sutharshan Rajasegarar & Lei Pan

  2. The University of Melbourne, Melbourne, Australia

    Christopher Leckie & Sarah M. Erfani

  3. RMIT University, Melbourne, Australia

    Jeffrey Chan

Authors
  1. Siqi Xia
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  2. Sutharshan Rajasegarar
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  3. Christopher Leckie
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  4. Sarah M. Erfani
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  5. Jeffrey Chan
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  6. Lei Pan
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Corresponding author

Correspondence to Siqi Xia .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Xia, S., Rajasegarar, S., Leckie, C., Erfani, S.M., Chan, J., Pan, L. (2023). EnSpeciVAT: Enhanced SpecieVAT for Cluster Tendency Identification in Graphs. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14178. Springer, Cham. https://doi.org/10.1007/978-3-031-46671-7_22

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  • DOI: https://doi.org/10.1007/978-3-031-46671-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46670-0

  • Online ISBN: 978-3-031-46671-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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