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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Proceedings of the 2002 ACM symposium on Applied computing
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Data Mining the Yeast Genome in a Lazy Functional Language
PADL '03 Proceedings of the 5th International Symposium on Practical Aspects of Declarative Languages
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Data arising from genomic and proteomic experiments is amassing at high speeds resulting in huge amounts of raw data; consequently, the need for analyzing such biological data --- the understanding of which is still lagging way behind --- has been prominently solicited in the post-genomic era we are currently witnessing. In this paper we attempt to analyze annotated genome data by applying a very central data-mining technique known as association rule mining with the aim of discovering rules capable of yielding deeper insights into this type of data. We propose a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to researcher in an incremental and interactive mode.