Nobutaka Mitsuhashi (email@example.com)
Haretsugu Hishigaki (firstname.lastname@example.org)
Toshihisa Takagi (email@example.com)
Human Genome Center, Institute of Medical Science, The University of Tokyo
4-6-1 Shirokanedai, Minato-ku, Tokyo 108 Japan
Knowledge discovery in large databases (KDD) is being performed in several application domains, for example, the analysis of sales data, and is expected to be applied to other domains. We propose a KDD approach to multipoint linkage analysis, which is a way of ordering loci on a chromosome. Strict multipoint linkage analysis based on maximum likelihood estimation is a computationally tough problem. So far various kinds of approximate methods have been implemented. Our method based on the discovery of association between genetic recombinations is so different from others that it is useful to recheck the result of them. In this paper, we describe how to apply the framework of association rule discovery to linkage analysis, and also discuss that filtering input data and interpretation of discovered rules after data mining are practically important as well as data mining process itself.