Elaris Computing Nexus
Intelligent Multimetric Agglomerative Clustering for Robust Waveform Pattern Discovery in High Dimensional Signal Spaces
Elaris Computing Nexus
Received On : 02 June 2025
Revised On : 12 August 2025
Accepted On : 28 August 2025
Published On : 16 September 2025
Volume 01, 2025
Pages : 143-156
Abstract
The waveform-based data is growing exponentially in the biomedical, communication, and industrial fields, which has generated an urgent need to develop unsupervised methods of learning the complex high-dimensional signal structures. Classical clustering techniques typically make use of one measure of similarity which does not reflect the inherent variability of waveforms patterns due to amplitude distortions, phase shifts and noise artifacts. In order to overcome such limitation, this paper will present an intelligent clustering framework called Intelligent Multimetric Agglomerative Clustering Network (IMAC-Net) which incorporates different distance metrics such as cosine, Euclidean, and cityblock into a single hierarchical structure. The proposed IMAC-Net is adaptive which benefits pattern discrimination in noisy signal conditions by weighting these metrics appropriately to balance between global and local similarity measures. Synthetic and real-world waveform dataset is used to verify the framework, and the performance is checked with the help of Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Score, and Cluster Purity. They are compared to the traditional methods, such as K-means, Hierarchical Clustering (single-metric), and Spectral Clustering. As experimental findings show, IMAC-Net exhibits better cluster cohesion, noise tolerance, and interpretation of waveform groups, which is much better as compared to the baseline models. The presented solution provides a generalizable and scalable solution to waveforms analytics, which opens the path to discovering patterns in high-dimensional, complex signal domains.
Keywords
Multimetric Clustering, Waveform Analysis, High-Dimensional Signal Processing, Unsupervised Learning, Agglomerative Hierarchical Model.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Aisling Yue Irwing and Alen Macline; Writing-Original Draft Preparation: Aisling Yue Irwing; Writing-Reviewing and Editing: Aisling Yue Irwing and Alen Macline; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Authors thanks to Faculty of Engineering and Computing for this research support.
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No funding was received to assist with the preparation of this manuscript.
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The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of Data and Materials
The data sets, employed in this study are publicly available and are used on a large scale in the research of waveform analysis. Controlled randomization was used to generate synthetic waveform data in order to produce realistic signal variations. Also, benchmark datasets were acquired in the UCR Time Series Classification Archive and PhysioNet repositories, which offer a variety of collections of biomedical and general waveform signals. All the processed data, the implementation scripts of the proposed IMAC-Net model could be obtained in the corresponding author under reasonable demand, and the entire reproducibility of the reported results could be ensured.
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Cite this Article
Aisling Yue Irwing and Alen Macline, “Intelligent Multimetric Agglomerative Clustering for Robust Waveform Pattern Discovery in High Dimensional Signal Spaces”, Elaris Computing Nexus, pp. 143-156, 2025, doi: 10.65148/ECN/2025014.
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© 2025 Aisling Yue Irwing and Alen Macline. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.