Abstract
Mapping tools applicable to big data of composite elements are designed based on a machine learning approach. The central method adopted is the multi-dimensional scaling (MDS). The data set is mapped onto a continuous surface such as a sphere. For checking to see the effectiveness of this method, preliminary experiments on the local optimality were conducted. Supported by those results, the main target for the application in this paper is the design for a spherical GUI (Graphical User Interface) which presents “must-watch” and “no-need” program clusters in TV big data. This GUI shows a certain genre of programs at around the North Pole. Programs having an opposite genre placed at around the South Pole. Since all-recording systems of TV programs are within the realm of home appliances, this GUI can be expected to be one of necessary tools for a video culture.
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Maejima, M., Yokote, R., Matsuyama, Y. (2012). Composite Data Mapping for Spherical GUI Design: Clustering of Must-Watch and No-Need TV Programs. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_32
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DOI: https://doi.org/10.1007/978-3-642-34500-5_32
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