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k均值聚类方法。 在给定一个有n个对象的数据集，划分聚类技术将构造数据进行k个划分，每一个划分代表一个簇，k小于等于n。-k-means clustering method. Given a set of n objects data, dividing the data clustering techniques to construct k partitions, each partition represents a cluster, k less than or equal n.
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K-MENAS算法是最简单的聚类算法，适合对于初学者的学习和改进使用-K-MENAS algorithm is a clustering algorithm is the most simple, suitable for beginners to learn and use improved
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k-menas算法是最简单的聚类算法，算法内详细介绍了各个函数和功能，对初学者很有借鉴意义-K-MEANS algorithm is one of the most simple clustering algorithm, there are different form, this is one of them, a reference for beginners
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实现对图片进行k均值聚类，也可以修改初始值，修改图片名直接可以运行-Implementation to k-means clustering of images, can also modify the initial value, the images can be run directly
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用JAVA实现k-means算法，其中聚类方法使用余弦相似度,带运行界面。完美运行。-Using JAVA k-means algorithm, clustering method using the cosine similarity
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simple k-means implementation with Persian comments
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samples about clustering in matlab :
K-means algorithm
K-medoids algorithm
DBSCAN algorithm
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K均值聚类算法，对风电机组功率数据进行聚类分析，包括详细的程序说明。
只要把这两个文件放入一个空文件夹下，在MATLAB中执行m文件，就可得到聚类结果。-K-means clustering algorithm, the wind turbine power data clustering analysis, including a detailed descr iption of the procedures. As long as these two files into an empt
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改进的K-means聚类算法。用于处理空间聚类问题。-The improved K means clustering algorithm.To deal with spatial clustering problem.
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java写的k-means，随机选择聚类中心-the realizationg of K-means clustering algotithm based on Java,with random selection of clustering centers
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实现kernel k-means 聚类，可以处理非线性数据-Implement kernel k-means clustering, can handle nonlinear data
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对k-means算法的优化，通过优化初始聚类中心的选择-The optimization of K-means algorithm by improving the selection of initial clustering centers
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这是一个k-means聚类算法，将一个四维量（比如有明确物理意义的花瓣长宽花萼长宽)按照几个中心点分成几类-This is a k-means clustering algorithm, a four-dimensional volume (such as a clear physical meaning petals calyx length and width aspect) is divided into several categories according to several ce
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标准的数据挖掘聚类算法 k均值聚类 k-means聚类 严格按照标准算法执行 简单高效-Standard data mining clustering algorithm k-means clustering In strict accordance with the standard algorithm is simple and efficient execution
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该算法是基聚类算法为K-means，然后再进行聚类集成，方法为投票法-The algorithm is based on clustering algorithm for K-means, and then the clustering ensemble, method for voting
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模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现-Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set
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k-means聚类算法 用C++实现 聚类采用数据为二维数据 保存在当前目录下的data.txt文件中-K-means clustering algorithm C++ implementation
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基于K-means聚类算法的图像分割 代码及实验测试结果-K-means clustering algorithm based on image segmentation code and some experimental results
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一种聚类算法：K-means聚类，实测绝对没有问题(A clustering algorithm: K-means clustering, no problem is absolutely no problem)
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利用K均值聚类对鸢尾花样本进行聚类的matlab程序，包含源代码、样本数据、聚类结果(The matlab program of clustering iris samples by K-means clustering, including source code, sample data and clustering results)
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