Data Mining Techniques in Soil Data Analysis for Effective Agriculture Data

The advancement in computers provided large amount of data. The task is to analyze the input data and obtain the required data which can be done by various data mining techniques. Present work focusses on analysis of relationships in spatial datasets are regional and there is a great need for regional regression methods that derive regional reflects different spatial characteristics of different regions. Naive Bayes, J48 (C4.5) and JRip Algorithms were used to analyse the data JRip reported to be simple, efficient classifier of soil data. The selected soil attributes were Nitrogen, Phosphorus, Calcium, Magnesium, Sulphur, Iron, Zinc, Potassium, PH and Humus. The attributes were predicted by linear regression. Even though all regressions provided almost equal results least Median Square depicts better results. This paper proposes a regional regression technique for regions that are defined by a categorical attribute, in particular soil type. The result is a series of hierarchically grouped regions according to their similarity.