Analysis of spatiotemporal data relationship using information granules

Analysis of spatiotemporal data relationship using information granules 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

Analysis of spatiotemporal data relationship using information granules

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-015-0386-x
Publisher site
See Article on Publisher Site

References

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