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@inproceedings{1366364,
author = {Lexa, Matej and Lapár, Radovan},
address = {New York},
booktitle = {Computational Advances in Bio and Medical Sciences (ICCABS), 2016 IEEE 6th International Conference on},
doi = {http://dx.doi.org/10.1109/ICCABS.2016.7802783},
keywords = {data mining; biomedical database; denormalization; visualization; correlation network},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York},
isbn = {978-1-5090-4199-2},
pages = {1-2},
publisher = {IEEE},
title = {Semi-automatic mining of correlated data from a complex database: Correlation network visualization},
url = {https://www.researchgate.net/publication/309157638_Semi-automatic_mining_of_correlated_data_from_a_complex_database_Correlation_network_visualization},
year = {2016}
}
TY - JOUR
ID - 1366364
AU - Lexa, Matej - Lapár, Radovan
PY - 2016
TI - Semi-automatic mining of correlated data from a complex database: Correlation network visualization
PB - IEEE
CY - New York
SN - 9781509041992
KW - data mining
KW - biomedical database
KW - denormalization
KW - visualization
KW - correlation network
UR - https://www.researchgate.net/publication/309157638_Semi-automatic_mining_of_correlated_data_from_a_complex_database_Correlation_network_visualization
L2 - http://ieeexplore.ieee.org/document/7802783/
N2 - In previous work we have addressed the issue of frequent ad-hoc queries in deeply-structured databases. We wrote a library of functions AutodenormLib.py for issuing proper JOIN commands to denormalize an arbitrary subset of stored data for downstream processing. This may include statistical analysis, visualization or machine learning. Here, we visualize the content of the Thalamoss biomedical database as a correlation network. The network is created by calculating pairwise correlations through all pairs of variables, whether they be numerical, ordinal or nominal. We subsequently construct the network over the entire set of variables, clustering variables with similar effects to discover group relationships between the various biomedical characteristics. We use a semi-automatic procedure that makes the selection of all pairs possible and discuss issues of dealing with different types of variables. This is done either by limiting the analysis to numerical and ordinal ones, or by binning their values into intervals of values. Knowledge extracted from the data in this mode can be used to select variables for statistical models, or as markers of medically interesting conditions.
ER -
LEXA, Matej a Radovan LAPÁR. Semi-automatic mining of correlated data from a complex database: Correlation network visualization. Online. In \textit{Computational Advances in Bio and Medical Sciences (ICCABS), 2016 IEEE 6th International Conference on}. New York: IEEE, 2016, s.~1-2. ISBN~978-1-5090-4199-2. Dostupné z: https://dx.doi.org/10.1109/ICCABS.2016.7802783.
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