Implementation of Classification System for Dry Zone Plantation Based on ID3 Algorithm

Abstract

Classification is data analysis process that can be used to extract models describing important data classes or predict future data. Classification of large data sets is an important data mining problem. Decision tree, mainly used for classification purpose, is a classifier method in the form of a tree structure. Decision tree algorithms are a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree. The data set of plantation system for Dry Zone is used to provide the aim of the system. The aim of this paper is to present how to construct decision trees (ID3), show its rules and evaluate the performance of model and to test the unknown data set. An accuracy method, hold-out, is allowed to use in this system for the validity of rules.