Distributed Data Mining System for Renal Data Analysis to Improve the Performance of Expert System
Data mining techniques that scale up to large and physically distributed renal data and its role in improving the performance of the expert system are investigated here. The Data mining system aim to discover patterns and extract useful information from facts recorded in databases. The number and variety of data sources is dramatically increasing. Information is ambiguous and possibly erroneous due to the dynamic nature of the information sources. One means of acquiring knowledge from databases is to apply various data mining algorithms that compute descriptive representation of data. Classifiers at local sites used to compute over the stationary data sets in parallel.