Description: 一种高效的聚类算法给定要聚类的N的对象以及N*N的距离矩阵(或者是相似性矩阵), 层次式聚类方法的基本步骤(参看S.C. Johnson in 1967)如下:-An Efficient Algorithm for the cluster must be the object of N and N * N distance matrix (or similarity matrix), the hierarchical clustering method the basic steps (see S. C. Johnson in 1967), as follows : Platform: |
Size: 432218 |
Author:毛显锋 |
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Description: 我自己编写的分层聚类算法，类内采用最大距离，类间采用最小距离实现-myself prepared by the Hierarchical clustering algorithm, the largest category within distance between categories of use to achieve minimum distance Platform: |
Size: 820 |
Author:张成 |
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Description: 我自己编写的分层聚类算法，类内采用最大距离，类间采用最小距离实现-myself prepared by the Hierarchical clustering algorithm, the largest category within distance between categories of use to achieve minimum distance Platform: |
Size: 1024 |
Author:张成 |
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Description: 一种高效的聚类算法给定要聚类的N的对象以及N*N的距离矩阵(或者是相似性矩阵), 层次式聚类方法的基本步骤(参看S.C. Johnson in 1967)如下:-An Efficient Algorithm for the cluster must be the object of N and N* N distance matrix (or similarity matrix), the hierarchical clustering method the basic steps (see S. C. Johnson in 1967), as follows : Platform: |
Size: 432128 |
Author:毛显锋 |
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Description: 1. 分层次聚类法（最短距离法）
2. 最简单的聚类方法
3. 最大距离样本
4. K 平均聚类法（距离平方和最小聚类法） -1. Hierarchical clustering method (the shortest distance method) 2. The simplest clustering method 3. The maximum distance the sample 4. K average clustering method (distance from the square and the smallest clustering method) Platform: |
Size: 47104 |
Author:math |
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Description: 聚类算法：最短距离算法。对给定的数据集进行自底向上的层次的分解，直到某种条件满足而已。缺陷在于一旦一个步骤完成，它就不能被撤消这个严格的规定是有用的，由于不用担心组合数目的不同选择，计算代价会较小。-Clustering Algorithm: the shortest distance algorithm. For a given data set to the level of bottom-up decomposition, until certain conditions are fulfilled it. Once the defect is a step towards completion, it can not be undone this strict requirement is useful to not have to worry about as a result of combination of the number of different options for calculating the price will be smaller. Platform: |
Size: 4096 |
Author:刘嘉良 |
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Description: 该综述介绍了分层次聚类法，最大距离样本，K平均聚类法等聚类方法的思路。-Summary of the introduction of the hierarchical clustering method, the greatest distance from the sample, K the average clustering method, such as the idea of clustering method. Platform: |
Size: 47104 |
Author:小雨 |
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Description: CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported.-CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported. Platform: |
Size: 258048 |
Author:tra ba huy |
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Description: Matlab数据统计和分析的程序，包含下面所列的多种算法的
MultiLineReg 用线性回归法估计一个因变量与多个自变量之间的线性关系
PolyReg 用多项式回归法估计一个因变量与一个自变量之间的多项式关系
CompPoly2Reg 用二次完全式回归法估计一个因变量与两个自变量之间的关系
CollectAnaly 用最短距离算法的系统聚类对样本进行聚类
DistgshAnalysis 用Fisher两类判别法对样本进行分类
MainAnalysis 对样本进行主成分分析-Matlab data and analysis procedures, listed below contain a variety of algorithms MultiLineReg estimated by linear regression of a dependent variable with a number of independent variables of the linear relationship between PolyReg estimation using a polynomial regression with dependent variable a variable relationship between CompPoly2Reg using quadratic polynomial regression method is estimated that fully a dependent variable and the two since the relationship between variables with the shortest distance CollectAnaly hierarchical clustering algorithm for clustering of samples with Fisher two types of DistgshAnalysis discriminant method to classify samples on the samples MainAnalysis principal component analysis Platform: |
Size: 3072 |
Author:Wade |
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Description: 用vs实现的层次聚类分析，是基于距离的算法，-Achieved with vs hierarchical clustering analysis algorithm based on distance, Platform: |
Size: 3222528 |
Author:zhangni |
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Description: 此源程序为层次聚类中AGNES聚类法算法部分，测试数据须自己输入，测试前最好先看下源码
测试数据只有2个属性，可根据自己需求修改数据结构体属性个数，与对象间欧式距离计算函数
本源码若算法复杂度有可改进的地方或有BUG请高手指出，计算500条以上的数据时须耐心等待结果
-The source code for the hierarchical clustering algorithm in part AGNES clustering method, the test data required to enter their own test before the best look at the source under test data only two properties, according to their property needs to modify the number of data structures, and objects evaluate the function of the Euclidean distance between the source if the complexity of the algorithm are areas of improvement or a BUG, please master pointed out that more than 500 data calculated to be patient and wait for the results Platform: |
Size: 2048 |
Author:odile zhu |
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Description: 一种分层聚类算法，类内采用最大距离，类间采用最小距离实现-A hierarchical clustering algorithm, within the class with the maximum distance between the smallest distance class realize Platform: |
Size: 1024 |
Author:bingo |
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Description: 演示了最大距离,最小距离,平均距离的分级聚类及其分类结果展示。-hierarchical clustering base on maximum distance,minumum distance and average distance.
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Size: 1024 |
Author:CHEN |
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Description: 数据聚类 (英语 : Cluster analysis) 是对于静态数据分析的一门技术，在许多领域受到广泛应用，包括机器学习，数据挖掘，模式识别，图像分析以及生物信息。聚类是把相似的对象通过静态分类的方法分成不同的组别或者更多的子集（subset），这样让在同一个子集中的成员对象都有相似的一些属性，常见的包括在坐标系中更加短的空间距离等。-Data Clustering (English: Cluster analysis) for a static data analysis technique has been widely applied in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the similar objects into different groups, or more subsets (subset) by the method of static classification, so let some of the properties in the same subset of member objects have similar common ones in the coordinate system more short space of distance. Platform: |
Size: 1024 |
Author:黄毅 |
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Description: 使用VC++对给出的数据分别进行均值聚类、平均距离聚类、最小距离聚类和最大距离聚类运算，进行分析比较。-VC++ use of the data presented were mean clustering, average distance clustering, minimum and maximum distances the cluster clustering operation, were analyzed and compared. Platform: |
Size: 5120 |
Author:杨洁 |
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Search - hierarchical clustering distance