Processing Imprecise Database Queries by Fuzzy Clustering Algorithms

Nowadays database management systems are one of the most critical resources in every company. Despite advanced possibilities of SQL, relational database management systems do not support flexible query conditions. Main assumptions of this work were two facts. First, that real data not representing random distribution (white noise), but have natural trend to granularity. The second one, that in everyday contacts we do not using strict defined conditions. The second feature lead us to use fuzzy logic which closer representing natural communication. First gives us opportunity to automatically construct functions defining membership to discrete groups based only on data distribution. The problem of extending database systems with natural language expressions is a matter of many research centers. The basic idea of presented research is to extend an existing query language and make database systems able to satisfy user needs more closely. This paper deals mostly with gaining imprecise information from relational database systems. Presented concept is based on fuzzy sets and automatic clustering techniques that allow to build membership function and fuzzy queries. Thanks to applied solutions, the relational database system is more flexible, and similar to natural way of communication.