Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining knowledge at multiple concept levels
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of multilevel gene association rules from microarray and gene ontology
Information Systems Frontiers
Bioinformatics
Mining Biomedical Knowledge Using Chi-Square Association Rule
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Cross-product extensions of the Gene Ontology
Journal of Biomedical Informatics
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The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.