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test3
- K-means聚类算法实现策略源代码实现，可以聚类分析一维数据-K-means source programe
k-meansClustering
- 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。-Where k-means clustering, is given in advance the number of classes contained in the original data, then the data contained similar characteristics grouped into a single class.
a4da35a45805
- 动态聚类的k-means图：正确的程序分出的输入数据-K-means clustering dynamic figure: the correct procedures to separate input data
k_means
- 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
k-means
- K均值算法，将数据矩阵命名为data，设置聚类簇个数k，可对多维数据进行聚类。-K mean algorithm, the data matrix is named data, set the number of clusters K, can be used to cluster the multi-dimensional data.
xin-k-means
- 此程序中直接是关于具体数据的聚类划分，直接给出相应的数据。-This program is directly of clustering on specific data directly gives the corresponding data
RBF-k
- RBF-k均值聚类算法的matlab程序和样本数据，可用于RBF-k均值聚类算法的仿真。-RBF-k-means clustering algorithm matlab program and sample data, can be used to simulate the RBF-k-means clustering algorithm.
k
- 用K均值聚类分析把多组数据分成两类 本程序为给定20组数据（用矩阵A表示）分成B、C两组。-K-means clustering analysis of the multiple sets of data into two categories This program is given 20 sets of data (represented by the matrix A) into B, C groups.
K-Means
- matlab kmeans 程序说明与数据源代码-matlab kmeans source
k-means
- matlabk-means聚类多维数据分析-matlabk-means clustering multidimensional data analysis
K-Means-master
- 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
k-means
- 机器学习中的kmeans算法用西瓜数据集4.0作为数据进行测试-Machine learning algorithms with watermelon kmeans data set 4.0 as the data for testing
k-junzhi
- 通过对K-均值算法的编程实现，加强对该算法的理解和认识。提高自身的知识水平和编程能力，认识模式识别在生活中的应用。 算法思想K-均值算法的主要思想是先在需要分类的数据中寻找K组数据作为初始聚类中心，然后计算其他数据距离这三个聚类中心的距离，将数据归入与其距离最近的聚类中心，之后再对这K个聚类的数据计算均值，作为新的聚类中心，继续以上步骤，直到新的聚类中心与上一次的聚类中心值相等时结束算法。-By programming K- means algorithm implementation, s
k-means
- java实现kmeans算法，方便数据挖掘相关人员更直观了解整个算法的思想及实现过程-java achieve kmeans algorithm to facilitate data relevant personnel more intuitive understanding of the whole idea of mining algorithms and implementation process
K-means_C
- 用K-means算法实现数据聚类。首先利用C随机产生800个数据，并将这800个数据作为一组训练样本；其次利用K-means的原理跟方法将这组样本聚类成8个类，从而实现数据的分类。-Data Clustering with K-means Algorithm. First, 800 data were randomly generated by C and the 800 data were used as a set of training samples. Secondly, the K-m
k-means
- 简单实现聚类算法中的经典k-menans算法，实现数据是二维数据- U7B80 u5B5 u5B9 u7B0 u803A u7R09 u7B09 u7B09
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)
kmeans
- 基于k均值的无监督聚类算法，输出有各个样本的类别标签，目标函数在每次迭代后的值，聚类中心以及聚类区间。内有测试数据，点击 test.m 可以完美运行。(The unsupervised clustering algorithm based on K means outputs the class labels of each sample, the value of the target function after each iteration, the clustering center a
kmean
- 负荷聚类，通过对负荷数据进行处理，提取典型用电方式，然后对典型用电方式进行聚类(load pattern clustering)
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)