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

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.

Journal

International Journal of Machine Learning and CyberneticsSpringer Journals

Published: Jun 17, 2015

References

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