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java写的k最邻近算法，是数据挖掘的基本算法之一。-Java write the k nearest neighbor algorithm, is one of the basic algorithm of data mining.
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KNN，k近邻算法，内附测试数据集，机器学习实战源码-KNN, k nearest neighbor algorithm, enclosing the test data set, machine learning practical source
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KNN算法，用于数据挖掘中数据离散化处理-K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning
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数据挖掘中经典的KNN（K-最近邻）算法，导入即可运行-Data Mining the classical KNN (K- nearest neighbor) algorithm, you can import operation
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本代码主要用了多标签K近邻方法（MLKNN）实现对多标签数据进行分类-This code mainly spent more than a label K-nearest neighbor method (MLKNN) to achieve the multi-label data classification
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C++实现数据挖掘的贝叶斯分类方法和k-近邻分类方法，有源码，有示例数据，有说明文档-C++ implementation of data mining methods and Bayesian classification k- nearest neighbor classification method, there is source code, sample data, documentation
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该程序是用python编写一个K近邻算法，通过该例子可以掌握K近邻算法，是学习数据挖掘的一个高效的算法。-The program is written in python a K-nearest neighbor algorithm, this example can grasp the K-nearest neighbor algorithm, a learning data mining and efficient algorithms.
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k近邻算法，可以简单准确的对数据，按照给定点与设定点的欧氏距离大小，进行分类-K nearest neighbor algorithm, can be simple and accurate data, in accordance with the set point to the point and the Euclidean distance of the set point size, classification
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在数据挖掘、人工智能等领域中，都常用到KD树来进行K近邻查找。本程序是自己用C++实现的一个KD树来进行的K近邻查找程序，包含建树和查找。文件中附有测试文件。-In data mining, artificial intelligence and other areas, it is commonly used to KD tree to find K nearest neighbor. This procedure is K neighbor Finder C++ they used to a
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模式识别中的k近邻算法，经过测试，运行结果很好。
最小距离分类器 : 它将各类训练样本划分成若干子类，并在 每个子类中确定代表点 。测试样本的类别则以其与这些代表点距离最近作决策。该方法的缺点是所选择的代表点并不一定能很好地代表各类，其后果将使错误率增加。(The k nearest neighbor algorithm in pattern recognition has been tested and the result is very good.
Minimum distance c
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基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。
经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set.
After PCA dime
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