Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A Novel Method of Combined Feature Extraction for Recognition
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Tell me something I don't know: randomization strategies for iterative data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Fusion of fMRI, sMRI, and EEG data using canonical correlation analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Bioinformatics
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
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Recent advances in high throughput data collection and imaging technologies have resulted in the availability of diverse biomedical datasets that capture complementary information pertaining to the biological processes in an organism. Biomarkers that are discovered by integrating such datasets obtained from case-control studies have the potential to elucidate the biological mechanisms behind complex human diseases. Of particular importance are interaction-type integrative biomarker, which are biomarkers whose features can explain the disease when taken together, but not when considered individually. We propose a pattern mining based integrative framework (PAMIN) to discover these interaction-type integrative biomarkers from diverse case control datasets. PAMIN first finds patterns from individual datasets to capture the available information separately and then combines these patterns to find integrated patterns (IPs) consisting of variables from multiple datasets. We also use several interestingness measures to characterize the IPs into specific categories. Using synthetic and real data we compare the IPs found using our approach with those found by CCA and discriminative-CCA (dCCA). Our results indicate that PAMIN is able to discover interaction type integrated patterns that these competing approaches cannot find.