Data Mining using Inductive Modeling Approach

The rate at which organizations are acquiring data is getting out of proportion and managing such data so as to infer useful knowledge that can be put to use is increasingly becoming important and challenging. Data Mining (DM) is one such relatively recently technology that has emerged that is employed in inferring useful knowledge that can be put to used from a vast amount of data. This paper proposes a new design methodology which is a hybrid of differential evolution (DE) and Group Method of Data Handling (GMDH) for self-organizing data mining. The new hybrid implementation is applied to the data mining activity of prediction of soil moisture, which is an aspect of hydrology. Experimental results indicate that the proposed approach is useful for data mining technique for forecasting hydrological data.