CARIBIAM: constrained Association Rules using Interactive Biological IncrementAl Mining.
This paper analyses annotated genome data by applying a very central data-mining technique known as Association Rule Mining (ARM) with the aim of discovering rules and hypotheses capable of yielding deeper insights into this type of data. In the literature, ARM has been noted for producing an overwhelming number of rules. This work proposes 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 investigators in an incremental and interactive manner.
Rahal
Imad
I
Computer Science Department, College of St. Benedict and St. John's University, Collegeville, MN 56321, USA. irahal@csbsju.edu
Rahhal
Riad
R
Wang
Baoying
B
Perrizo
William
W
D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D003628:Database Management Systems; D030541:Databases, Genetic; D016247:Information Storage and Retrieval / Q000379:methods*; D008969:Molecular Sequence Data; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D014584:User-Computer Interface