一种基于实现 k-近邻算法的研究

摘要

分类算法与聚类算法是机器学习中的两种重要算法，这两种算法在机器学习中是不可缺少的。在当今信息万变的海量的数据中，没有分类算法和聚类算法的机器对数据的识别变得困难或者不能识别未知和有已知的各种数据，因此分类算法在当今变得十分重要。 k‐近邻算法和决策树算法在机器学习方法中都是经典数据分类，作者在查阅了相关的文献与资料之后，将两分类算法相互地有机地结合起来，并且使用决策树的算法实现了 k‐近邻算法的实例分类。%The classification algorithm and the clustering algorithm are two important algorithms in machine learning , the two algorithm is indispensable in machine learning .In today’s changing mass data ,it is difficult or impossible to identify the unknown and known for various data identification of data without classification algorithm and the clustering algorithm of the machine ,so the classification algorithm becomes very important in today’s .k‐nearest neighbor algorithm and decision tree algorithm in machine learning method is the classical data classification ,after the author related literature and data in consulting ,case classification two classification algorithm mutually combine and organic using decision tree algorithm to a‐chieve the k‐nearest neighbor algorithm .