Abstract
Data analysis especially data with space and time feature in a human-centric way requires interpretable representation of data. With this motivation, we present a granular way of data analysis in which the data and the relationships therein are described through a collection of sets or fuzzy sets (information granules). In this paper, data are described by semantically meaningful descriptors-information granules over the space and time domain. The design process is guided by information granulation and degranulation. Thus a performance index used to obtain the best combination of information granules becomes a crucial issue. The effectiveness of the algorithm is demonstrated by experiments on two kinds of synthetic data and data from Alberta agriculture website.
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Acknowledgments
Support from the Natural Science Foundation of China (NSFC) 61305100 and 61203283, 71401026, the Youth Science Foundation of Communication University of China 3132015XNG1501, YXJS201529, the technology research of cinema management system and collaborative network service platform (2012BAH02F04), National Key Technology Support Program (2013BAH66F02) are gratefully appreciated.
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Song, M., Shang, W., Wang, L. et al. Analysis of spatiotemporal data relationship using information granules. Int. J. Mach. Learn. & Cyber. 8, 1439–1446 (2017). https://doi.org/10.1007/s13042-015-0386-x
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DOI: https://doi.org/10.1007/s13042-015-0386-x