Abstract: Data mining is the process of finding new patterns. Classification is the method of generalizing known structure to pertain to new data. Classification using a decision tree is achieved by routing from the root node until arriving at a leaf node. To model classification process, decision tree is used. Also there are many classification algorithms available in literature but decision trees is the most commonly used because of its ease of implementation and easier to understand compared to other classification algorithms. This paper begins with the basic concepts of Classification and the method of the decision tree. Then, this paper analyses the data of arable land area, rural labor and the gross output value of agriculture about 10 cities of India based on the decision tree, and implement clustering analysis method to discrete data during the process of data mining compared to the traditional classification methods. Finally, carry out generalization conceptual process from the results of the classification [1].

Keywords:classification concepts, Decision Tree, clustering, agriculture, production data set.