Description: K均值算法的一个改进算法，简单实用，但是有点简单。-K-means algorithm of an improved algorithm is simple and practical, but a bit simple. Platform: |
Size: 80469 |
Author:石支柱 |
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Description: 由于K－均值聚类算法局部最优的特点，而模拟退火算法理论上具有全局最优的特点。因此，用模拟退火算法对聚类进行了改进。20组聚类仿真表明，平均每次对K结果值改进8次左右，效果显著。下一步工作：实际上在高温区随机生成邻域是个组合爆炸问题（见本人上载软件‘k－均值聚类算法’所述），高温跳出局部解的概率几乎为0，因此正考虑采用凸包约束进行模拟聚类，相关工作正在进行。很快将奉献给各位朋友。-as K-means clustering algorithm for optimal local characteristics, and simulated annealing algorithm theory with the characteristics of the global optimum. Thus, simulated annealing algorithm for clustering improvements. Cluster Group of 20 simulations show that the average value of K results improved about eight times, the results are obvious. The next step : In fact, in high temperature generated random neighborhood is a combination of explosives (see my software on the 'k-means clustering algorithm' mentioned above), high-temperature solution of partial out almost zero probability, it is considering the use of convex hull bound for simulation cluster, the work under way . Soon dedication to the ladies. Platform: |
Size: 5120 |
Author:韩磊 |
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Description: K均值算法的一个改进算法，简单实用，但是有点简单。-K-means algorithm of an improved algorithm is simple and practical, but a bit simple. Platform: |
Size: 79872 |
Author:石支柱 |
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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) Platform: |
Size: 1024 |
Author:阿兜 |
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Description: 自己花费整整两个星期写出来的关于数据挖掘K-means算法的论文，论文中详细的进行了论述并提出了改进后的算法以及对比欢迎下载-Of them have to spend a full two weeks to write out data mining on the K-means algorithm thesis paper discussed in detail and put forward an improved algorithm and welcome contrast to Download Platform: |
Size: 60416 |
Author:zjz |
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Description: 改进的k均值算法，可以加速运行时间，详见Using the Triangle Inequality to Accelerate k-Means-Improved k-means algorithm, can accelerate the running time, see Using the Triangle Inequality to Accelerate k-Means Platform: |
Size: 13312 |
Author:xz |
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Description: 现有的几个网络拓扑随机发生器，其实很难生成理想的网络拓扑结构，其主要原因在于很难控制节点的疏密和间距。我们提出来的这个改进算法，在随机抛撒节点的时候使用了K均值聚类，由本算法作为网络拓扑发生器，网络节点分布均匀且疏密得当，边的分布也比较均衡-The few existing random network topology generator, is in fact very difficult to generate the desired network topology, the main reason it is difficult to control the node density and spacing. We put forward the improved algorithm, throw in random nodes when using the K-means clustering, by the algorithm as a network topology generator, network nodes and spacing evenly distributed properly, the edge of a more balanced distribution of Platform: |
Size: 2048 |
Author:ben |
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Description: 改进的k-means方法，对聚类的实例节能型加权 少数类多数类的函数-Improved k-means method for clustering a small number of examples of energy-saving type of weighted majority of types of function Platform: |
Size: 9216 |
Author:zhangwen0927 |
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Description: 模式识别经典方法之一 K均值算法（KMeans）进行图像分割 有完善的图形界面 可以通过柱状图的形式动态展示聚类程序-One of the classical method of pattern recognition K means algorithm (KMeans) for image segmentation has improved graphical interface can be dynamically displayed in the form of histogram clustering procedure Platform: |
Size: 147456 |
Author:langger |
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Description: 用c程序和matlab分别试验一种k-means改进算法，按照方法选取聚类中心点，事实证明，这种改进是有效的。-Matlab with c procedures and were experimenting with an improved k-means algorithm, in accordance with the method of selecting cluster center, the facts show that this improvement is effective. Platform: |
Size: 59392 |
Author:王建梅 |
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Description: 本实验基于K-Means聚类算法思想实现了字符分割，因为车牌规定是7位的，所以K取7。另外本实验对K-Means算法进行了改进，充分考虑了初始点的设置及迭代结束条件。实验结果证明这种改进的K-Means算法实现车牌字符分割是快速、有效的。-In this study, K-Means clustering algorithm based on the ideology of the character segmentation, because the license plate requirement is 7, so K = 7. Besides this, this K-Means algorithm for improved full account of the initial point of the set and iteration conditions. Experimental results show that the improved K-Means algorithm license plate character segmentation is fast and effective. Platform: |
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Author:王娟 |
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Description: 本文详细地介绍了k-means算法和一种改进的K-means算法，即基于成对约束的Cop-kmeans算法。该算法结合了Must-Link和Cannot-Link约束，数据对象在这两类约束的“监督”下进行划分。-This paper introduces the k-means algorithm and an improved K-means algorithm, which is based on pairwise constraints Cop-kmeans algorithm. The algorithm combines the Must-Link and the Cannot-Link constraints, these two types of data objects bound in the "supervision" under the division. Platform: |
Size: 15360 |
Author:李呈利 |
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Description: 基于K—Means的中文文本聚类算法的研究和实现。中文文本聚类的主要技术，特征选择，共享最近邻的K-Means的改进算法。基于k-Means的实现和实验。-Based on the Chinese version of K-Means clustering algorithm and implementation. The main technology of Chinese Text Clustering, feature selection, shared nearest neighbor Improved K-Means algorithm. Based on k-Means of implementation and experiments. Platform: |
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Author:火麒麟 |
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Description: K-means，PSOKM，ACKM三种聚类算法，数据集适用于K-means，在PSO上效果不好，AC有改进(Including K-means, PSOKM, ACKM three clustering algorithms, the data set applies to K-means, clustering on PSOKM is not good, clustering in ACKM has improved.) Platform: |
Size: 8192 |
Author:waitsong
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Description: 对K-means聚类算法的改进X-means算法。针对K值如何设定的问题，利用BIC准则判断聚类点数量。同时附有两个初始聚类点的选择程序，自己选吧。(The improved X-means algorithm for K-means clustering algorithm. In view of the problem of how to set the K value, the number of clustering points is judged by the BIC criterion. At the same time, there are two initial clustering points of the selection program, choose it yourself.) Platform: |
Size: 4096 |
Author:kbc1993 |
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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.) Platform: |
Size: 3072 |
Author:伯鸾君 |
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Search - improved k means