Imad Rahal
ABSTRACT
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.
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Index Terms
- Incremental interactive mining of constrained association rules from biological annotation data with nominal features
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