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kmeans
- k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the clu
kMeansCluster
- k-Means 算法接受输入量 k ；然后将 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。聚类相似度是利用各聚类中对象的均值所获得一个 “ 中心对象 ” （引力中心）来进行计算的。-K-Means algorithm accepts input amount of K then the object n data is divided into k cluster so that the obtained clusteri
CanopyExm
- Canopy聚类算法是一个将对象分组到类的简单、快速、精确地方法。每个对象用多维特征空间里的一个点来表示。这个算法使用一个快速近似距离度量和两个距离阈值 T1>T2来处理。 Canopy聚类算法能快速找出应该选择多少个簇，同时找到簇的中心，这样可以大大优化 K均值聚类算法的效率 。-Canopy is a clustering algorithm to group objects into simple categories, fast, accurate method. Each obj
DataMiningCluster-master
- 数据挖掘的聚类算法实现 Implementation of text clustering algorithms including K-means, MBSAS, DBSCAN-data mining cluster
ap-Code
- 本程序为仿射传播聚类的算法，相比较于K均值聚类不需要确定聚类个数，且对初始聚类中心不敏感-This procedure for affinity propagation clustering algorithm, compared to K-means clustering is not required to determine the number of clusters, and is not sensitive to the initial cluster centers
kmeans
- k均值聚类方法。 在给定一个有n个对象的数据集，划分聚类技术将构造数据进行k个划分，每一个划分代表一个簇，k小于等于n。-k-means clustering method. Given a set of n objects data, dividing the data clustering techniques to construct k partitions, each partition represents a cluster, k less than or equal n.
kmo
- k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the clu
kmeans
- 在数据挖掘中， k-Means 算法是一种 cluster analysis 的算法，其主要是来计算数据聚集的算法，主要通过不断地取离种子点最近均值的算法。-K-means algorithm
Cluster_K-means
- k中心算法的基本过程是:首先为每个簇随意选择一个代表对象，剩余的对象根据其与每个代表对象的距离（此处距离不一定是欧氏距离，也可能是曼哈顿距离）分配给最近的代表对象所代表的簇；然后反复用非代表对象来代替代表对象，以优化聚类质量。聚类质量用一个代价函数来表示。当一个中心点被某个非中心点替代时，除了未被替换的中心点外，其余各点被重新分配。-The basic process k center algorithm is: First free to choose a delegate object fo
change_adap_k_mean
- 利用K均把图片分割成3部分。无需注明基点。-use adaptive K means to segment an image into 3 region without the need to specify cluster number.
kmeans_fast_Color
- 加快K均把图片分割成3部分。cluster number 已被固定。-K means clustering algorithms with faster response to segment an image into specific 3 region
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
MonTestRandom
- k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apri
alg
- 基改进的K-means聚类算法对已知初始聚类中心，对质心点进行求解，并考虑到不同点的权重问题。-Base Improved K-means clustering algorithm known initial cluster centers, centroid point is solved, taking into account the different points of the right to re-issue.
gounang
- 信号维数的估计，基于K均值的PSO聚类算法,包括AHP，因子分析，回归分析，聚类分析。- Signal dimension estimates, K-means clustering algorithm based on the PSO, Including AHP, factor analysis, regression analysis, cluster analysis.
kmeans.tar
- k means , able to create cluster with information data base files.-k means , able to create cluster with information data base files.
km4
- MATlab聚类分析代码，K-MEANS聚类三维分析-MATlab cluster analysis code, three-dimensional cluster analysis K-MEANS
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
SCS_kmeans_mri
- The program is done by using k-means clustering method. We first read the image and then by assigning the cluster values we find distance and then updating it and shows the output
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.