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k-means 算法的工作过程说明如下:首先从n个数据对象任意选择 k 个对象作为初始聚类中心;而对于所剩下其它对象,则根据它们与这些聚类中心的相似度(距离),分别将它们分配给与其最相似的(聚类中心所代表的)聚类;然后再计算每个所获新聚类的聚类中心(该聚类中所有对象的均值);不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点:各聚类本身尽可能的紧凑,而各聚类之间尽可能的分开。下面给出我写的源代码。-work process k-means algorithm is as follows: First, choose k objects from n data objects as the initial cluster centers while for the rest of the other objects, according to the similarity (distance) with those of their cluster centers, They were assigned to the most similar (represented by the cluster center) clustering then calculated for each cluster received new cluster center (the cluster mean all objects) repeats this process Until the beginning of a standard measure function convergence. MSE is generally used as the standard measure function k clustering has the following characteristics: each cluster itself as compact as possible, and to separate between the clusters as possible. Here is what I wrote the source code.
Update : 2024-05-05 Size : 2048 Publisher : xiaojade

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k_means算法,可以用于聚类分析,用来识别系统的状态。-It is kmeans. we can use it to identify the operation of the system.
Update : 2024-05-05 Size : 1024 Publisher : 冯莉

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模式识别C++ K均值算法,采用容器,对数据进行分类-k_means arithmetic
Update : 2024-05-05 Size : 959488 Publisher : 洋琴

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K-means algorithm and it s performance assesment in matlab.
Update : 2024-05-05 Size : 1024 Publisher : Mahtab

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利用c语言编写,用k_means 分类方法实现对数据分类。-k-means
Update : 2024-05-05 Size : 178176 Publisher : yuqing

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K均值聚类算法,希望对大家有用 Kmeans cluster algorithm It is possible to perform -Kmeans cluster algorithm It is possible to perform
Update : 2024-05-05 Size : 1024 Publisher : Process

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经典的k均值聚类算法,用于无监督学习和数据分类,广泛应用于数据处理和模式识别领域-Classical k-means clustering algorithm for unsupervised learning and data classification, widely used in data processing and pattern recognition
Update : 2024-05-05 Size : 2048 Publisher : 张磊

利用BmpFile类实现图像的变化检测以及K均值分类,并且实现了一种改进后的K均值分类方法-change detect,K_means
Update : 2024-05-05 Size : 2477056 Publisher : 吴青

为解决原始K2means 算法随机选取初始聚类中心对聚类结果的影响较大的不足,提出了改进算法-In order to solve the shortage of original K2means algorithm randomly selects a larger effect of initial clustering centers of clustering results, puts forward the improved algorithm
Update : 2024-05-05 Size : 194560 Publisher : 德静

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k-means聚类分析matlab实现,有详细注释和测试数据-k-means clustering matlab realization, detailed notes and test data
Update : 2024-05-05 Size : 48128 Publisher :

Kmeans 算法的详细介绍word文档,以及相应的Kmeans 的matlab实现代码-Kmeans introductions and matlab code
Update : 2024-05-05 Size : 23552 Publisher : 李伟

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K-mean 算法是 J.B.MacQueen 在 1967 年提出的,是到目前为止应用最广泛最成熟的一种聚类分析方法。因该算法具有简单快速、适于处理大数据集等优点,目前,已被广泛应用于科学研究和工业应用中。-K-mean algorithm is JBMacQueen proposed in 1967, is by far the most mature and most widely used of a clustering analysis. Because the algorithm is simple and fast, suitable for processing large data sets and other advantages of the present, it has been widely used in scientific research and industrial applications.
Update : 2024-05-05 Size : 1024 Publisher : lqsayf

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数据聚类算法 kmeans 算法 ,使用MATLAB实现,文中的代码注释为中文,更易于实现-data mining algorithm , clustering algorithm , k_means algorithm
Update : 2024-05-05 Size : 1024 Publisher : qiong hiong

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kmeans 算法的改进算法 名字为kmeans++算法,参考的文献为 kmeans++: an efficient algorithm。。。。算法他人所写,这里上传,以方便同行传阅-this is a algorithm named k_means++. reference the paper : an efficient algorithm ...
Update : 2024-05-05 Size : 2048 Publisher : qiong hiong

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使用kmeans算法,对图像进行分类,并且显示分类结果-classify dataset
Update : 2024-05-05 Size : 21504 Publisher : jackokie

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hierarchical clustering protocole
Update : 2024-05-05 Size : 2048 Publisher : zouzou

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k-means聚类算法,该算法已经经过验证,描述了聚类的过程-k-means clustering algorithm, which has been validated, we describe the process of clustering
Update : 2024-05-05 Size : 1024 Publisher : 752163381

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一种聚类方法,K均值聚类。一种简单的聚类方法-K means
Update : 2024-05-05 Size : 1024 Publisher : 杨斌

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k-means聚类,聚类分析在人工智能方面多有用处,而k-means方法是最简单、最基本的一种,程序是k-means聚类方法的C++实现-k-means clustering
Update : 2024-05-05 Size : 2048 Publisher : qingchun720

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K_means聚类分析,内含1个图片的3维数据聚类分析例子-K_means cluster analysis
Update : 2024-05-05 Size : 23552 Publisher : 陈昊
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