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Analysis of Data Mining Techniques for Agriculture Data

Data mining is the process of extracting important and useful information from large sets of data. The goal of the data mining process is to extract knowledge from an existing data set and transform it into a unique human understandable format for some advance use. Data mining in agriculture is in relation to novel research field. Some efficient techniques can be developed and tailored for solving complex agricultural problems using data mining. Agriculture and allied activities constitute the single largest component of India’s gross domestic product, contributing nearly 25% of the total and nearly 60% of Indian population depends on this life’s precise profession. Due to vagaries of climate factors the agricultural productivities in India are continuously decreasing over a decade. The reasons for this were studied mostly using regression analysis. In this paper an attempt has been made to compile the research findings of different researchers who used agriculture data. This paper summarizes the application of data mining techniques such as k-means, bi clustering, k nearest neighbor, Neural Networks, Support Vector Machine and Naïve Bayes Classifier in the agriculture field.