Description: 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or) Platform: |
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Author:阿兜 |
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Description: 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n objects assigned K to 000 category, making such objects within the similarity between the largest category of the similarity between the smallest. Platform: |
Size: 1408 |
Author:yili |
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Description: k均值聚类的算法，这是通用程序！欢迎借鉴使用-k average value gathers a kind of algorithm, this is the
general routine! Welcome to profit from uses Platform: |
Size: 20480 |
Author:李凯 |
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Description: 在Visual C++.NET2003平台下实现聚类算法中的K-MEANS算法，对随机生成的点进行了聚类。使用单文档结构，并将聚类结果显示出来。-In the Visual C++. NET2003 platform clustering algorithm to achieve the K-MEANS algorithm, on randomly generated points in the cluster. Use a single document structure, and clustering results are displayed. Platform: |
Size: 63488 |
Author:杨维斌 |
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Description: 我自己做的聚类搜索引擎,使用了K-mean和K-NN聚类算法-I have to do the clustering search engine, using the K-mean and K-NN clustering algorithm Platform: |
Size: 214016 |
Author:李烨 |
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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: 蛙蛙的中文文本聚类，主要采用k-means算法。wawa s text cluster using C#.-蛙蛙Chinese text clustering, the main use of k-means algorithm. wawa s text cluster using C#. Platform: |
Size: 16384 |
Author:陈石 |
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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: C-means 算法，模式识别中常用到的C均值聚类算法，使用VC开发，版权归开发者所有-C-means algorithm, pattern recognition used in the C-means clustering algorithm, the use of VC development, belongs to all developers Platform: |
Size: 578560 |
Author:门昌骞 |
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Description: RBF（径向基神经网络）网络是一种重要的神经网络，RBF网络的训练分为两步，第一步是通过聚类算法得到初始的权值，第二步是根据训练数据训练网络的权值。RBF权值的初始聚类方法较为复杂，比较简单的有K均值聚类，复杂的有遗传聚类，蚁群聚类等，这个RBF网络的程序是基于K均值聚类的RBF代码。(RBF (radial basis function network) is an important neural network. The training of RBF network is divided into two steps. The first step is to get the initial weight by clustering algorithm, and the second step is to train the weight of the network according to training data. The initial clustering method of RBF weights is relatively complex. There are relatively simple K mean clustering, complex genetic clustering, ant colony clustering, etc. this RBF network is based on K means clustering RBF code.) Platform: |
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Author:老外
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Description: 介绍了k-means 均值聚类，能很好的将离散的点，聚类成几个指定的聚合点。(The K-means mean clustering is introduced, and the discrete points can be well clustered into several designated aggregation points.) Platform: |
Size: 2048 |
Author:asq109 |
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Description: K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from 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 cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.) Platform: |
Size: 1024 |
Author:invoker`Z |
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Description: k均值聚类程序，虽然matlab中也有自带的，但是这个速度不错。(K mean clustering program, although matlab also has its own, but this speed is good.) Platform: |
Size: 1024 |
Author:xx123123123123 |
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Search - k-mean clustering