Abstract
Despite the drop in disk prices in recent years, the costs associated with disks still represent the primary costs for largeβscale databases such as those used in data warehouses. Compression and storage of such databases is seen as an effective means to reduce these costs. The authors' research group has already proposed a compression method that exchanges the priority for rules and data after extracting rules latent in data by using knowledge discovery as a database compression method which can access a database in its compressed state. However, this proposed method has the problem of producing differences in the compression ratio due to the priority with which the extracted rules are used for compression. Simply finding all the combinations for the priority for the use of such rules is not practical, and so in this paper the authors propose a rule selection method which provides comparatively good compression ratios without excessive computational requirements, then demonstrate the validity of their method using experimental results. Β© 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(11): 11β20, 2001