搜索资源列表
k-meansgaijin
- 数据挖掘中的一个聚类算法k-means。-data mining clustering algorithm of a k-means.
K-Mean1
- 编写K-均值聚类算法程序，对下图所示数据进行聚类分析(选k=2)-prepare K-means clustering algorithm, the data shown in the chart below cluster analysis (EAC k = 2)
Fuzzy-k-means
- 模糊核聚类及几篇文章，用于数据和图像的模糊聚类分割，效果还行-nuclear fuzzy clustering and articles for data and image segmentation fuzzy clustering, the results were OK
fk-means
- 数据挖掘中模糊k均值算法，matlab工具编写。-data mining fuzzy k-means algorithm, Matlab tool for the preparation.
K-means-clustering-algorithm
- k均值聚类是最著名的划分聚类算法，由于简洁和效率使得他成为所有聚类算法中最广泛使用的。给定一个数据点集合和需要的聚类数目k，k由用户指定，k均值算法根据某个距离函数反复把数据分入k个聚类中。-K-means clustering is one of the most famous partitioning clustering algorithm, due to the simplicity and efficiency makes him become the most widely used
K-means-Clustering
- 根据模拟数据进行K均值聚类实验，有原始数据，聚类代码和相关图片，程序可用！-Analog data according to K-means clustering experiment, the original data, clustering code and picture, the program is available!
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.py
- k－means算法的python直接通过word2vec生成的向量空间数据放入，可直接获得所需要的聚类结果。可以自己设定输出的类别数目。-K-means algorithm directly into the vector space data generated by word2vec, you can directly obtain the desired clustering results. You can set the number of output categories.
K-means
- K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。-K-means clustering algorithm is hard, is a typical prototype-based clustering method objective function representative, which is a method of data points to a certain d
All_Clu
- K-means，PSOKM，ACKM三种聚类算法，数据集适用于K-means，在PSO上效果不好，AC有改进(Including K-means, PSOKM, ACKM three clustering algorithms, the data set applies to K-means, clustering on PSOKM is not good, clustering in ACKM has improved.)
kmeans
- 有关K-means的小程序，可以直接导入数据作聚类，也可自动生产数据(K-means applet, you can directly import data for clustering, you can also automatically produce data)
k-means算法2
- 使用该算法可以实现数据的聚类分析，非常适合初学者。(The algorithm can be used to achieve clustering analysis of data, ideal for beginners.)
kmean
- 一个学习k均值聚类的实例，代码实现了其基本原理，简单易懂，带有测试，训练数据集，可直接上手操作(A learning k-means clustering example, the code to achieve its basic principles, easy to understand, with a test, training data set can be used directly)
AnalysisKSVDbox
- K-SVD可以看做K-means的一种泛化形式，K-means算法总每个信号量只能用一个原子来近似表示，而K-SVD中每个信号是用多个原子的线性组合来表示的。 K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分类等应用。(K-SVD can be regarded as a generalized form of K-means. The total K-means algorithm can only approximate one signal for each sem
Kmeans
- 机器学习聚类K-means算法，用于无标签数据的聚类(Machine learning clustering K-means algorithm is applied to cluster of unlabeled data.)
20170110_KMeans
- 在花卉数据集上，用Java实现的简单K-means算法。(In flower dataset, a simple K-means algorithm is implemented by Java.)
K均值聚类
- K均值聚类算法-对数据进行聚类分析，适合数据处理(k means clustering algorithm)
k-means算法的Matlab实现以及Iris数据集
- k-means算法实现以及Iris数据集(Implementation of K-means algorithm and Iris data set)
k-means-for-iris
- 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)
can_use_kmeans
- K-means对iris数据集进行分类，可画出3维分类图(K-means to classify iris data set)