A Topological-Indicators-Based k-Means Clustering Algorithm and Its Application in Time Series Data: A Case Study on Sea Level Variability in Peninsular Malaysia

Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, particularly when applied to datasets with mixed groups or significant noise. In this study, we analyzed monthly se...

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Veröffentlicht in:	IEEE access Jg. 13; S. 46514 - 46533
Hauptverfasser:	Lin, Zixin, Zulkepli, Nur Fariha Syaqina, Bin Mohd Kasihmuddin, Mohd Shareduwan, Gobithaasan, Rudrusamyr
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Analytical models Biological system modeling Cluster analysis Clustering Clustering algorithms computational topology Data analysis Data points Datasets environmental sustainability Homology Machine learning Noise Point cloud compression Sea level sea level variability Sea measurements Tides Time series Time series analysis Topology Vector quantization Vectors Malaysia
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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