Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

Ariza-Jiménez, Leandro; Villa, Luisa F.; Quintero, O. Lucía

doi:10.1007/978-3-030-31019-6_23

Cited by 2 publications

(2 citation statements)

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“…The visualization of fuzzy clusters is also challenging, as soft clustering algorithms result in more complex cluster structures. By introducing a membership network in [29], which is based on the fuzzy partition matrix and represents fuzzy clustering, the visual understanding should be improved. It is shown how the elements involved in this type of complex data clustering structure interact with each other without relying on a visualization of the input data itself.…”

Section: Visualization Techniques Literature Reviewmentioning

confidence: 99%

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Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters

Vahldiek,

Klawonn

2024

Applied Sciences

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Fuzzy clustering, as a powerful method for pattern recognition and data analysis, often produces complex results that require careful examination of individual clusters. In this paper, advanced visualization techniques are presented that aim to facilitate the analysis of fuzzy clustering results by focusing on the evaluation and interpretation of individual clusters. The presented approach is based on the development of cluster-centric visualization techniques that consider the inherent uncertainty of fuzzy clustering results. The novelty is an assessment of individual clusters with the proposed visualizations. In general, three cluster-centered visualization techniques are presented. These approaches are intended not only to illustrate the overall structure of the fuzzy clustering results but also to enable detailed individual cluster analysis. The performance of the presented visualization techniques is demonstrated by their application to real data sets from different areas. The results show that the techniques provide an effective way to judge individual clusters in fuzzy clustering results for complex data structures.

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Section: Visualization Techniques Literature Reviewmentioning

confidence: 99%

“…From [19,22,25] only the ideas for clustering are used. The basics are also the membership degree, which refers to [20,21,29]. Since the CCV techniques also use a circular display, refs.…”

Section: Visualization Techniques Literature Reviewmentioning

confidence: 99%