Development of privacy preserving fuzzy data mining methods

Data mining or discovering knowledge from a database are processes that analyzing the unknown relationships in the that database. Recently, the problem of privacy preserving occurs due to increasing of studies on this area and sharing the large information over internet and media environments. Some techniques have been developed fort his purpose, however many of these studies are working based on crisp (Bloolean) values. In real world, the databases of applications consist of quantitative values. For instance; in analyzing the leukocyte in one?s blood, the main goal is to find the ratio of leukocyte instead of whether leukocyte is exists. The count of studies on the privacy preserving on such quantitative databases is very small.In this thesis, we have implemented the privacy preserving on quantitative databases by using fuzzy logic techniques. Today, the most application area of such databases are about sickness knowledge. We have developed two algorithms fort he problems about the relations and clustering of these databases. The application software for a case study on the database of breast cancer have been developed.