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Description: 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code.
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Author: pbt |
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Description: 一个CURE聚类算法 应用了K中心点算法 采用空间坐标聚集 -a clustering algorithm is applied to the K-center space coordinates is used to gather
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Author: karoe |
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Description: 这是我帮一个本科生做的毕业设计，实现的数据挖掘的k均值和k中心算法，其中包含了我做的两个二维的数据集，感觉要预先知道k的参数值，不是很方便-This is what I do to help an undergraduate graduation Design, Implementation of the Data Mining mean k and k center algorithm, which includes me to do two two-dimensional data sets, feeling to know beforehand the value of the parameter k is not easy
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Size: 157928 |
Author: 孟繁宇 |
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Description: 这是我帮一个本科生做的毕业设计，实现的数据挖掘的k均值和k中心算法，其中包含了我做的两个二维的数据集，感觉要预先知道k的参数值，不是很方便-This is what I do to help an undergraduate graduation Design, Implementation of the Data Mining mean k and k center algorithm, which includes me to do two two-dimensional data sets, feeling to know beforehand the value of the parameter k is not easy
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Size: 157696 |
Author: 孟繁宇 |
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Description: 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code.
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Size: 50176 |
Author: pbt |
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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。 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)
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Author: 阿兜 |
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Description: 一个CURE聚类算法 应用了K中心点算法 采用空间坐标聚集 -a clustering algorithm is applied to the K-center space coordinates is used to gather
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Size: 1932288 |
Author: karoe |
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Description: DataMining软件(集成了关联规则、k-均值聚类、模糊聚类、k-中心点聚类四种算法)
-DataMining software (integrated association rules, k-means clustering, fuzzy clustering, k-center clustering of four algorithms)
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Author: Jim |
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Description: ：将K—means算法引入到朴素贝叶斯分类研究中，提出一种基于K—means的朴素贝叶斯分类算法。首先用K—
me．arks算法对原始数据集中的完整数据子集进行聚类，计算缺失数据子集中的每条记录与 个簇重心之间的相似度，把记
录赋给距离最近的一个簇，并用该簇相应的属性均值来填充记录的缺失值，然后用朴素贝叶斯分类算法对处理后的数据
集进行分类。实验结果表明，与朴素贝叶斯相比，基于K—means思想的朴素贝叶斯算法具有较高的分类准确率。-: K-means algorithm will be introduced to the Naive Bayesian Classifier study, a K-means based on the Naive Bayesian classification algorithm. First of all, with K-me. arks algorithm focus on the raw data of the complete data subset of the cluster, the calculation of missing data for each subset of records and the similarity between the cluster center of gravity to the nearest record assigned to a cluster, and the corresponding attributes of the cluster means to fill the missing value record, and then use Naive Bayes classification algorithm to deal with the data set after classification. The experimental results show that compared with the Naive Bayes, K-means based on the thinking of Naive Bayes algorithm has higher classification accuracy.
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Author: 李浩 |
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Description: 数据挖掘中 K中心点算法 测试数据为iris 数据库采用sql server 聚类算法-Data mining algorithms in test data for the K center iris database using sql server clustering algorithm
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Author: 赵新星 |
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Description: 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 to accept input k then n-k of data objects into a cluster in order to make the cluster available to meet: the object of the same cluster in the high similarity the similarity of different objects in clusters smaller. Cluster similarity is the use of the mean of each cluster obtained by objects in a " central object" (center of gravity) to be calculated.
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Author: Chenguang |
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Description: 这是一个K中心算法的Matlab程序，很多用-This is a Matlab algorithm K center procedures, many with
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Size: 9216 |
Author: yrc |
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Description: k-means 算法
step1 初始化K个质心
step2 将所有的点分配给最近的质心
step3 更新质心
step4 若质心都没用变化,则停止,否则返回step2
-k-means algorithm is initialized step1 step2 K a center of mass of all the points assigned to the nearest centroid centroid step3 step4 update no use if the change in the center of mass, then stop, otherwise return to step2
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Author: vince |
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Description: K-means算法是最为经典的基于划分的聚类方法，是十大经典数据挖掘算法之一。K-means算法的基本思想是：以空间中k个点为中心进行聚类，对最靠近他们的对象归类。通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。-K-means algorithm is based on the division of the classic clustering method, is ten classic one of data mining algorithm. K-means the basic idea of the algorithm is: to the space K point as the center of the cluster analysis, near their object classification. Through the iterative method, each successive update clustering center value, until get the best clustering results.
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Author: 彭立军 |
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Description: 基于K-means聚类算法的社团发现方法
先定义了网络中节点关联度,并构建了节点关联度矩阵, 在此基础上给出了一种基于 K-means聚类算法的复杂网络社团发现方法。
以最小关联度原则选取新的聚类中心, 以最大关联度原则进行模式归类,直到所有的节点都划分完为止, 最后根据模块度来确定理想的社团数-K-means clustering algorithm based on the association discovery
To define a network node correlation, and build the node correlation matrix in this basis, given a K-means clustering algorithm based on a complex network of associations that way.
The principle of the minimum correlation to select a new cluster center to the principle of maximum correlation pattern classification until all the nodes are divided until the end, the last under the module to determine the degree of the ideal number of community
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Author: maverick |
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Description: K中心聚类算法 ，声明：本源程序由网络搜集整理，不承担技术及版权问题!-K center clustering algorithm, the statement: This source collected by the network, does not bear the technical and copyright issues!
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Size: 2048 |
Author: cyz |
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Description: 通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics of the sample frame to replace the class with the center, reducing the dimension of the nuclear matrix, and then use Sparse KPCA method for feature extraction of the nuclear matrix.
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Author: piano |
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Description: Using K-center to segmentation
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Author: katition |
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Description: K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。K-means算法以欧式距离作为相似度测度，它是求对应某一初始聚类中心向量V最优分类，使得评价指标J最小。算法采用误差平方和准则函数作为聚类准则函数。(The K-means algorithm is a hard clustering algorithm, which is representative of the prototype based objective function clustering method. It is the distance from the data point to the prototype as the objective function of the optimization, and the method of using the function to find the extremum is used to get the adjustment rules of the iterative operation. The K-means algorithm takes Euclidean distance as the similarity measure, it is to find the V optimal classification corresponding to an initial cluster center vector, so that the evaluation index J is the smallest. The error square sum criterion function is used as a clustering criterion function.)
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Size: 1024 |
Author: Daizy7 |
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Description: 基于数据密度自动计算最佳K聚类中心，对数据进行聚类(The best K clustering center is automatically calculated based on data density to cluster data.)
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Author: 伯鸾君 |
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