Motion Classification Using Proposed Principle Component Analysis Hybrid K-Means Clustering

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ISSN Print: 1947-3931
ISSN Online: 1947-394X
www.scirp.org/journal/eng
E-mail: eng@scirp.org

Citations

"Motion Classification Using Proposed Principle Component Analysis Hybrid K-Means Clustering"
written by Ching Yee Yong, Rubita Sudirman, Nasrul Humaimi Mahmood, Kim Mey Chew,
published by Engineering, Vol.5 No.5B, 2013

has been cited by the following article(s):

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[3]	Feature selection for unsupervised machine learning of accelerometer data physical activity clusters–A systematic review
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[4]	An Automatic Detection of Rugby Union Player Motions Using Time Series Analysis
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[5]	Model Selection Based on Bayes Factor/Seleção de modelo com base no Bayes Factor
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[1]	Interstride Variation in EEG Power Spectra of Younger and Older Adults Walking at a Range of Gait Speeds IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2026 DOI:10.1109/TNSRE.2026.3656061

[2]	Gait speed-related changes in electrocortical activity in younger and older adults Journal of Neurophysiology, 2025 DOI:10.1152/jn.00544.2024

[3]	Feature selection for unsupervised machine learning of accelerometer data physical activity clusters – A systematic review Gait & Posture, 2021 DOI:10.1016/j.gaitpost.2021.08.007

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