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KMeans2
- kmeans算法的Java实现。算法接受参数 k 然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足:同一聚类中的对象相似度较高 而不同聚类中的对象相似度较小。-k means algorithm is implemented in Java. Receiving algorithm parameter k and n data objects entered beforehand into k clusters in order to satisfy such cluste
Kjunzhi
- K-均值聚类算法的源文件，可以解决基本的聚类问题-K- means clustering algorithm source files, you can solve the basic problem of clustering
moshishibie
- 基于matlab的模式识别基础实例源代码，包括贝叶斯分类器，Fisher线性判别，主成分分析，k均值聚类等。-Based on pattern recognition based matlab examples of source code, including Bayesian classifier, Fisher linear discriminant analysis, principal component analysis, k-means clustering.
Kmeans
- K均值聚类算法是先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心-K-means clustering algorithm is to randomly K objects as the initial cluster centers. Then calculate the distance of each object and each seed cluster centers, assigning each objec
K_means
- K均值聚类算法 属于模式识别的一种基本算法 在此没有与opencv关联-K-means clustering algorithm A basic pattern recognition algorithm is not associated with this opencv
k_means
- 这是数据挖掘中的k均值聚类算法，用java语言编写的，对于搞聚类的人士很有帮助-This is the data mining k-means clustering algorithm, using java language, for persons engaged in clustering helpful
ClusterAnalysis_2014.11.4
- 模式识别的聚类分析。K均值聚类算法是先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。这个过程将不断重复直到满足某个终止条件。终止条件可以是没有（或最小数目）对象被重新分配给不同的聚类，没有（或最小数目）聚类中心再发生变化，误差平方和局部最小。-Pattern recognition clustering
KMeans
- k均值聚类，有聚类数目，聚类中心，实现有效聚类。-k-means clustering, there is the number of clusters, cluster centers achieve effective clustering.
MRIxiaonaonaogan
- 从脑部MRI图像中提取小脑和脑干，分别采用k均值聚类算法，阈值分割，形态学操作，分水岭算法，区域合并-Brainstem and cerebellum extracts the brain MRI images were used to k-means clustering algorithm, thresholding, morphological operations, watershed algorithm, region merging, etc.
MRIzuoyounaobanqiu
- 从脑部MRI图像中提取左右脑半球，分别采用k均值聚类算法，阈值分割，形态学操作，分水岭算法，区域合并-Extracted the brain MRI image left and right brain hemispheres, respectively k-means clustering algorithm, thresholding, morphological operations, watershed algorithm, region merging, etc.
k
- k-means实现聚类算法的程序，简单易懂，适合初学者-K-means clustering algorithm to achieve the procedure, easy to understand, suitable for beginners
k_means
- k均值聚类 是一种最基础的聚类方法，就是把看起来最集中，最不分散得到标签分配到输入训练样本中去，求局部最优解。-k-means clustering is one of the most basic clustering method is to look the most concentrated, most non-dispersible label assigned to obtain input to the training sample, find local optima.
kmeans
- k均值聚类分割算法的matlab代码，初学者使用-k-means clusterings
Matlabalgorithmlibrary
- MATLAB算法库，包含K均值聚类-分类模型，logistics回归-用于决策，非线性拟合，回归分析模型，支持向量机SVM-用于分类决策，主成分分析PCA-用于决策，TXT格式，适用于数学建模-MATLAB algorithm library, including K-means clustering- classification model, logistics regression- for decision making, nonlinear fitting, regression an
clusterTest
- 用java语言实现k均值聚类的代码demo，可直接运行,无需调试。-Using java language k-means clustering code demo, it can be run directly without debugging.
kMean
- k均值聚类的matlab代码，包含有样本间距的矩阵求解程序。-k-means clustering matlab code, comprising sample matrix solvers pitch.
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
- K均值聚类算法是先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。这个过程将不断重复直到满足某个终止条件。-k-means algorithm
800370
- 国外的一个图像K均值聚类的C++源代码,非常容易集成和使用-An image abroad k-means clustering of the c++ source code, very easy to integrate and use
KMeans
- K目标跟踪中的K均值聚类法，根据预测坐标进行初始化-K means algorithm