分类是数据挖掘 、机器学习 和模式识别 中一个重要的研究领域。分类的目的是学会一个分类模型 (称作分类器)，该模型能把未知类别的数据项映射到给定类别中。目前发展较成熟的几种分类算法 如决策树、神经网络、贝叶斯方法、遗传算法等。分类具有广泛的应用，例如医学诊断、信用卡系统的信用分级、图像模式识别等。本毕业设计通过使用鸢尾属植物(IRIS)数据集，对当前数据挖掘中具有代表性的优秀分类算法进行分析和比较，总结出了各种算法的特性，为使用者选择算法或研究者改进算法提供了依据。-Classification of data mining, machine learning and pattern recognition is an important area of ​ ​ research. The purpose of classification is to learn a classification model (called classifier), the model can unknown types of data items mapped to a given category. Currently the more mature types of classification algorithms such as decision trees, neural networks, Bayesian methods, genetic algorithms. Classification with a wide range of applications such as medical diagnosis, credit card system, credit rating, image pattern recognition. The graduation project by using the Iris genus (IRIS) data sets, data mining has on the current outstanding representative of the analysis and comparison of classification algorithms, summed up the characteristics of various algorithms for researchers to improve the user selection algorithm or algorithm provided.