Mining Spatial Object Associations for Scientific Data
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 902-906.
Abstract
In this paper, we present efficient algorithms to discover spatial associations among features extracted from scientific datasets. In contrast to previous work in this area, features are modeled as geometric objects rather than points. We define multiple distance metrics that take into account objects’ extent. We have developed algorithms to discover two types of spatial association patterns in scientific data. We present experimental results to demonstrate the efficacy of our approach on real datasets drawn from the bioinformatic domain. We also highlight the importance of the discovered patterns by integrating the underlying domain knowledge.
@inproceedings{ijcai2005p177,
title = {Mining Spatial Object Associations for Scientific Data},
author = {Yang, Hui and Parthasarathy, Srinivasan and Mehta, Sameep},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {902--906},
year = {2005},
url = {https://ijcai.org/proceedings/2005/177},
}
TY - CPAPER TI - Mining Spatial Object Associations for Scientific Data AU - Yang, Hui AU - Parthasarathy, Srinivasan AU - Mehta, Sameep T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 902 EP - 906 UR - https://ijcai.org/proceedings/2005/177 ER -Copied
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