Publication details

Semi-automatic mining of correlated data from a complex database: Correlation network visualization

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LEXA Matej LAPÁR Radovan

Year of publication 2016
Type Article in Proceedings
Conference Computational Advances in Bio and Medical Sciences (ICCABS), 2016 IEEE 6th International Conference on
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords data mining; biomedical database; denormalization; visualization; correlation network
Description In previous work we have addressed the issue of frequent ad-hoc queries in deeply-structured databases. We wrote a library of functions 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.
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