Description: 聚类研究，实现了基于距离，基于密度和改进算法-clustering, based on the distance to achieve, based on density and improved algorithm Platform: |
Size: 73728 |
Author:建国 |
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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: 数据挖掘算法之一，基于代表点的CURE聚类算法，该算法先把每个数据点看成一类，然后合并距离最近的类，直至类个数为所要求的个数为止。-CURE cluster algorithm based on representive point,one of data mining algorithms,classifies each data as a category firstly, then unifies categories with the nearest distance into one until the number of class is coincidence with the classes demanded. Platform: |
Size: 45056 |
Author:黄镇 |
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Description: 聚类算法大部分都使用距离来计算相似度，本文探讨了无需使用距离的方法。-clustering algorithm used to calculate similarity distance, the paper discusses the need to use the distance. Platform: |
Size: 32768 |
Author:石支柱 |
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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: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
一个可以实现多种方法分类的软件，利用各个
对象的属性。决策树，距离、密度等-Weka is a collection of machine learning al gorithms for data mining tasks. The algorithms can either be applied directly to a dataset or ca lled from your own Java code. Weka contains tool 's for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for d eveloping new machine learning schemes. can be a real Categories are various methods of software, using all the attributes of objects. Decision Tree, distance, density, etc. Platform: |
Size: 15446016 |
Author:马何坛 |
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Description: 用VC++语言实现了基于距离，基于密度和改进的数据聚类算法。-VC language based on the distance, based on the density and improved data clustering algorithm. Platform: |
Size: 73728 |
Author:lixiaoqing |
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Description: 主要解决旅行商问题(traveling saleman problem,简称tsp，即已知n个城市之间的相互距离，现有一个推销员必须遍访这n个城市，并且每个城市只能访问一次，最后又必须返回出发城市，求解最短距离的遗传算法。-mainly to solve the traveling salesman problem (traveling saleman problem, listed tsp, known cities n between the distance from the existing one salesman must traveled n this city, and visit each city only once, finally starting to return to the city, for the shortest distance from the genetic algorithm. Platform: |
Size: 1024 |
Author:阳文 |
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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: K-MEANS算法
输入：聚类个数k，以及包含 n个数据对象的数据库。
输出：满足方差最小标准的k个聚类。
处理流程：
（1） 从 n个数据对象任意选择 k 个对象作为初始聚类中心；
（2） 循环（3）到（4）直到每个聚类不再发生变化为止
（3） 根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；
（4） 重新计算每个（有变化）聚类的均值（中心对象）-K-MEANS algorithm Input: cluster number k, and contains n data object database. Output: the minimum standards to meet the variance k-clustering. Deal flow: (1) a data object from the n choose k object as initial cluster centers (2) cycle (3) to (4) until a change in each cluster is no longer so far (3) according to each Clustering objects mean (central object), calculated for each object with these centers to object distance and in accordance with a minimum distance between a re-division of the corresponding object (4) re-calculated for each (change) clustering of the mean (central object ) Platform: |
Size: 3072 |
Author:快快 |
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Description: K聚类分析，通过使用欧式距离，K聚类方法显示聚类结果，用于分类-K cluster analysis, using Euclidean distance, K show the clustering results of clustering method for classification Platform: |
Size: 21504 |
Author:谢天培 |
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Description: 在模式识别中，尤其需要对一些样本进行分类，聚类分析是常用的方法，本程序基于最小最大距离的聚类原则实现对样本的聚类-At pattern recognition, in particular a number of samples required for classification, clustering analysis is a commonly used method, the procedure based on the smallest maximum distance of the cluster principle implementation on sample clustering Platform: |
Size: 2048 |
Author:文嘉俊 |
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Description: 聚类算法kmeans，比较简单的聚类算法，通过欧几里德距离确定聚类的标准，对二维的点进行聚类-Clustering algorithm kmeans, relatively simple clustering algorithm, through the Euclidean distance to determine the standard clustering of the points of two-dimensional clustering Platform: |
Size: 3072 |
Author:huang |
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Description: 先用C-均值聚类算法程序，并用下列数据进行聚类分析。在确认编程正确后，采用蔡云龙书的附录B中表1的Iris数据进行聚类。然后使用近邻法的快速算法找出待分样本X（设X样本的4个分量x1=x2=x3=x4=6；子集数l=3）的最近邻节点和3-近邻节点及X与它们之间的距离。-First C-means clustering algorithm procedures and with the following data for cluster analysis. After confirming the correct programming, using the book蔡云龙Table 1 in Appendix B of the Iris data clustering. And the use of a close neighbor of law to be fast algorithm to find sub-samples of X (for X samples four components x1 = x2 = x3 = x4 = 6 subset of the number of l = 3) of the nearest neighbor nodes and 3- neighbor nodes and X and the distance between them. Platform: |
Size: 1024 |
Author:jack |
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Description: 用Java语言实现的空间聚类分析程序，对离散点按照距离标准进行分类。-Java language with the spatial clustering analysis procedures, in accordance with the distance between discrete points of criteria. Platform: |
Size: 8192 |
Author:荆凯旋 |
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Description: 聚类是把一组个体按照相似性归成若干类别，即“物以类聚”。它的目的是使得属于同
一类别的个体之间的距离尽可能的小而不同类别上的个体间的距离尽可能的大。聚类方
法包括统计方法、机器学习方法、神经网络方法和面向数据库的方法。
-Clustering is a group of individuals as to the similarity in accordance with a number of categories, that is, " 物以类聚." Its purpose is to allow individuals fall into the same category as far as possible the distance between the different categories of small and the individual as much as possible the distance between the major. Clustering methods, including statistical methods, machine learning, neural network methods and database-oriented approach. Platform: |
Size: 1422336 |
Author:qingpeng yu |
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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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Search - clustering distance