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Description: The last step in training phase is refinement of the clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-clusters within the larger ones. -The last step in training phase is refinement of the clustersfound above. Although DynamicClustering counters all thebasic k-means disadvantages, setting the intra-cluster similarityr may require experimentation. Also, a cluster mayhave a lot in common with another, ie, sequences assignedto it are as close to it as they are to another cluster. Theremay also be denser sub-clusters within the larger ones.
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Size: 39936 |
Author: yznushuangyu |
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Description: 空间数据分析中最常用的是聚类分析,而K-MEANS算法是聚类分析中常用的,其主要思想是在给定的聚类数目下对多维(我做的是三维空间点)向量进行聚类,-Spatial data analysis is the most commonly used cluster analysis, while the K-MEANS algorithm is commonly used in cluster analysis, the main idea is to set the number of under the multi-dimensional clustering (I make the three-dimensional space-point) vector cluster,
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Size: 6144 |
Author: tangkezong |
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Description: 闭包传递法的一个函数以及一个求最大生成树的函数,用于模糊聚类分析(matlab)-Closure of the transfer method for a function and a function of the maximum spanning tree for the fuzzy cluster analysis (matlab)
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Size: 1024 |
Author: kkk |
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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 )
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Size: 3072 |
Author: 快快 |
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Description: k-均值聚类算法实现灰度图像分割，输入图像矩阵和聚类中心个数，返回为最终的聚类中心和图像中每个像素所属类的编号（对应于图像矩阵）-k-means clustering algorithm to achieve gray-scale image segmentation, the input image matrix and the number of cluster centers, the return for the final image of the cluster centers and their respective categories in each pixel number (corresponding to the image matrix)
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Size: 1024 |
Author: cc |
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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#.
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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
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Size: 21504 |
Author: 谢天培 |
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Description: 混合高斯模型
对于给定的数据，可以自动选择最佳聚类数目和聚类中心，并根据判决规则进行收敛，运算很快，非常方便-Mixed-Gaussian model for a given data, can automatically select the best cluster number and cluster centers, and in accordance with decision rules convergence, compute very fast, very convenient
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Size: 60416 |
Author: 辛芳芳 |
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Description: Demo HZ256 Cluster LCD2 CW31 SH v1 ICD
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Size: 474112 |
Author: 顾委找 |
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Description: 基于聚类的RBF 网设计算法,验证过了，仿真效果还不错-Cluster-based RBF network design algorithm, verified, the simulation effect is also good
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Size: 1024 |
Author: 余咏波 |
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Description: 学习传感器网络必须要看一看的文章，是对leach的后期版，集中式的分簇路由协议-Learning sensor network must take a look at the article, is to leach the latter version, centralized sub-cluster routing protocol
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Size: 348160 |
Author: lww |
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Description: 实现一个超级聚类程序,具有很好的参考价值.-The realization of a super-cluster procedure, with a very good reference value.
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Size: 27648 |
Author: 李德志 |
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Description: 本方法是一种自动的分类和聚类的方法，方法是最基本的实现，希望对向下的研究有用-This method is an automated classification and clustering methods, the most basic way is to realize, in the hope that down the research useful
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Size: 373760 |
Author: mike |
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Description: 新书推荐之分布式计算与并行计算：从集群到网格。2007年度出版的新书，对于从事并行计算的学生，老师，工作人员来说未尝不是一本好书。而且本书比较短小精悍，易懂，也可作为入门书籍阅读学习。-New Books of distributed computing and parallel computing: From Cluster to Grid. 2007 book, for parallel computing students, teachers, staff may not be a good book. But compare this book dapper, easy to understand, but also can be used as entry-books to read to learn.
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Size: 5151744 |
Author: gouyabin |
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Description: oracle 11g的文档，详细介绍11g中的grid,real application cluster等
-oracle 11g document, detailing 11g of grid, real application cluster, etc.
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Size: 2152448 |
Author: lihui |
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Description: 在Labview编程环境中，字符串与簇之间的转换的例子。-In the Labview programming environment, the string with the conversion between cluster examples.
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Size: 5120 |
Author: tianjing |
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Description: FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分。-FCM algorithm is a clustering algorithm based on the division of its thinking is that it is making is divided into clusters with the greatest similarity between the object, and different similarity between the smallest cluster. Fuzzy C-means algorithm is an ordinary C-means algorithm, the general C-means algorithm for data delineation is rigid, while the FCM is a soft fuzzy partition.
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Size: 368640 |
Author: Alpha |
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Description: 多移动机器人群集移动算法VC++实现源码-Multi-cluster Mobile Algorithm for Mobile Robot VC++ Realize source
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Size: 6170624 |
Author: 陈新 |
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Description: fcm是模糊c均值聚类算法，确定聚类中心以后，通过循环迭代，确定最佳聚类中心-FCM is a fuzzy c means clustering algorithm to determine the cluster center, the adoption cycle of iteration, to determine the optimal cluster center
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Size: 6144 |
Author: zhourl |
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Description: FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分。在介绍FCM具体算法之前我们先介绍一些模糊集合的基本知识。-FCM algorithm is a clustering algorithm based on the division of its thinking is that it is making is divided into clusters with the greatest similarity between the object, and different similarity between the smallest cluster. Fuzzy C-means algorithm is an ordinary C-means algorithm, the general C-means algorithm for data delineation is rigid, while the FCM is a soft fuzzy partition. FCM in the introduction prior to the specific algorithm we first introduce some basic knowledge of fuzzy sets.
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Size: 9216 |
Author: zhourl |
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