搜索资源列表
K-MEANS
- 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
k-means--segmentation
- 该代码实现了k-means聚类算法对图像的分类处理,同时通过RMSE和准确性对比对技术的表现和准确度进行了测试-The code implements the k-means clustering algorithm to classify the image. At the same time, the performance and accuracy of the technique are tested by RMSE and accuracy comparison.
K-Means
- K-means聚类算法以及代码实现 K-means聚类算法以及代码实现-K-means clustering algorithm and code implementation K-means clustering algorithm and code implementation
fuzzy-K-means
- fuzzy-K-means聚类算法源代码,常用于模糊聚类分析,算法能取得较好性能,简单有效,应用范围广-the fuzzy-K-means cluster program which can achieve good performance
K-means
- K-means聚类算法的matlab代码。-MATLAB code for k-means clustering algorithm.
kmeans2
- 此程序可以实现大型数据聚类算法,其中含有测试数据。(This program can achieve large data clustering algorithm, which contains test data)
K-Means PCA降维
- K-Means算法,不要求建立模型之后对结果进行新的预测,没有相应的标签,只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the character
K均值对图像进行聚类分析
- 用k-means算法对图像进行聚类,适合于初学者(K-means algorithm for clustering images, suitable for beginners)
99273863K-means-clustering-algorithm
- K-均值聚类算法。可自由输入初始聚类中心的个数和其坐标。(K- means clustering algorithm. The number of initial cluster centers and its coordinates can be freely entered.)
K聚类
- 聚类,这是一个用MATLAB编写的kmeans算法,主要用通常的聚类分析(Clustering, which is a MATLAB written in kmeans algorithm, mainly with the usual clustering analysis)
聚类k-means
- 一个非常简单的kmeans算法,主要用于聚类分析,用户仅需要输入聚类数(A very simple kmeans algorithm, mainly for clustering analysis, users only need to enter the number of clusters)
k-means
- 此代码可以对图像很好的聚类,文件里面有原始图像,也有聚类后的图像,聚类的效果挺好的,大家可以看看(This code can make a good clustering of images. In the file, there are original images, and there are also images of clustering. The effect of clustering is good. You can have a look at it)
clustering-index
- 欢迎使用和评述此工具箱,您的意见是对我们工作的支持。 此工具适合于不同有效性指标的性能比较,改进代码用于不同的应用问题等等。 (1) NCT的内容 NCT包括4个外部有效性指标和8种内部有效性指标,编制的程序文件"validity_Index.m"用于调用它们 (2) 主文件 "mainClusterValidationNC.m" 的内容 主文件设计为如何使用PAM聚类算法、如何使用有效性指标和方法来估计聚类个数。(H
1、K-means学习
- K-means算法MATLAB仿真,利用一副图像作为数据实现K聚类算法仿真(K-means algorithm, MATLAB simulation)
k-means
- k-means以空间中k个点为中心进行聚类,对最靠近他们的对象归类 c++实现代码(C++ implementation code of k-means)
kmeans
- 对数据和图像进行聚类分析,k-means聚类方法多应用于模式识别,人工智能,机器学习等方面(Clustering analysis of data and images, K-means clustering method should be used in pattern recognition, artificial intelligence, machine learning and so on)
Data_Clustering
- 对给定的一堆数据进行聚类,并进行类别标记(Clustering a given set of data)
cluster
- 快速搜索与发现密度峰值聚类方法来确定聚类中心(Clustering by fast search and find of density peaks)
KMeans
- 基于matlab语言编译的用于实现聚类的k-means函数(K-means function compiled by MATLAB language for clustering)
julei
- 聚类算法是给一大堆原始数据,然后通过算法将其中具有相似特征的数据聚为一类。 这里的k-means聚类,是事先给出原始数据所含的类数,然后将含有相似特征的数据聚为一个类中。(The clustering algorithm is given to a large number of original data, and then the data with similar features are gathered into a class by algorithm. The K-mean