Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Knowledge discovery in databases is the non-trivial extraction of potentially useful information in databases. Many of conventional methods extract knowledge from data in one place. However, it is often the case where data is generated and stored in two or more places. In this paper, we propose a method to extract fuzzy rules in an entire decentralized data by transferring only values necessary for the calculation process, without collecting all decentralized data. The characteristics of data in each place can be expressed by comparing the rules for the entire data with those for each data. We illustrate a result for experiments using Iris data by R.A.Fisher.
