Title: CARIBIAM: Constrained Association Rules using Interactive Biological IncrementAl Mining

Authors: Imad Rahal, Riad Rahhal, Baoying Wang, William Perrizo

Addresses: Computer Science Department, College of St. Benedict and St. John's University, Collegeville, 56321 MN, USA. ' Paediatrics Department, University of Iowa, Iowa City, 52242 IA, USA. ' Department of Mathematics/Computer Science, Waynesburg University, Waynesburg, 15370 PA, USA. ' Computer Science and Operations Research Department, North Dakota State University, Fargo, 58105 ND, USA

Abstract: 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.

Keywords: bioinformatics; yeast genome; association rule mining; ARM; incremental mining; interactive mining; P-tree technology; annotated genome data; data mining.

DOI: 10.1504/IJBRA.2008.017162

International Journal of Bioinformatics Research and Applications, 2008 Vol.4 No.1, pp.28 - 48

Published online: 17 Feb 2008 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article