Abstract
Data mining applications over on-body sensor data have earned great attention in recent years. We propose a novel Online Multi-divisive Hierarchical Clustering Method on on-body sensor data. Our method evolves tree-like top down hierarchy cluster, which splits and agglomerates clusters as needed. Experimental results prove a competing quality for our method over existing ones.
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Acknowledgment
This research was supported by the grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Chungbuk BIT Research-Oriented University Consortium): the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2010–0001732): the research grant of the Chungbuk National University in 2008.
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Musa, I.M.I. et al. (2010). Online Multi-divisive Hierarchical Clustering for On-Body Sensor Data. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_10
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_10
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