Abstract
In this paper, we have projected an efficient mining method for a temporal dataset of humanoid robot HOAP-2 (Humanoid Open Architecture Platform). This method is adequate to discover knowledge of intermediate patterns which are hidden inside different existing patterns of motion of HOAP-2 joints. Pattern-growth method such as FP (Frequent Pattern) growth, unfolds many unpredictable associations among different joint trajectories of HOAP-2 that can depict various kinds of motion. In addition, we have cross-checked our methodology over Webots, a simulation platform for HOAP-2, and found that our investigation is adjuvant to predict new patterns of motion in terms of temporal association rules for HOAP-2.
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© 2009 Springer-Verlag Berlin Heidelberg
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Singh, U., Singh, K.P., Nandi, G.C. (2009). Mining Temporal Patterns for Humanoid Robot Using Pattern Growth Method. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_43
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DOI: https://doi.org/10.1007/978-3-642-10646-0_43
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