Description: ISODATA算法是一种基于统计模式识别的非监督学习动态聚类算法，有较强的实用性。介绍了ISODATA算法原理和实现步骤，结合具体算例进行了聚类分析，得到了理想的分类结果，并分析了参数的选取将对分类结果产生的影响。
-ISODATA algorithm is a statistical pattern recognition based on non-supervised learning dynamic clustering algorithm has good practicability. Introduction of the ISODATA algorithm and the realization of these steps, combined with specific examples of cluster analysis, to be an ideal classification results and an analysis of the selected parameters will be the impact of classification results. Platform: |
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Description: 在Visual C++下实现聚类分析在图像识别中的应用。其中包括模糊聚类，遗传算法聚类分析，层次聚类和动态聚类等算法。可以运行，并有分类的源文件。是不错的研究素材。-In Visual C++ Achieve clustering analysis in image recognition applications. Including fuzzy clustering, genetic algorithm for cluster analysis, hierarchical clustering and dynamic clustering algorithm, etc.. Can run, and the classification of the source file. Is a good study material. Platform: |
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Author:dd |
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Description: 本程序是利用最大类间方差算法求解自适应阈值，对图像进行分割,非动态阈值-This procedure is the use of maximum between-cluster variance adaptive thresholding algorithm for image segmentation, non-dynamic threshold Platform: |
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Author:gyq |
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Description: ISODATA算法是一种基于统计模式识别的，非常经典的非监督学习动态聚类算法，有较强的实用性。ISODATA算法不仅可以通过调整样本所属类别完成样本的聚类分析，而且可以自动地进行类别的“合并”和“分裂”，从而得到类数比较合理的聚类结果。-ISODATA algorithm is based on statistical pattern recognition, and very classic dynamic clustering of non-supervised learning algorithm has good practicability. ISODATA algorithm can not only adjust the class to complete samples of cluster analysis of samples, and can automatically type of " merger" and " split" in order to get a few more reasonable type of clustering results. Platform: |
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Author:justinchan |
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Description: dynamic allocation of master-worker algorithm implementation in a cluster of personal computers with ANSI C Platform: |
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Author:kthomas_k |
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Description: C++中的ISODATA算法示例，ISODATA算法是一种基于统计模式识别、经典的动态聚类算法，有较强的实用性。ISODATA算法不仅可以通过调整样本所属类别完成样本的聚类分析，而且可以自动地进行类别的“合并”和“分裂”，从而得到类数比较合理的聚类结果。 -C++ examples of ISODATA algorithm, ISODATA algorithm is based on statistical pattern recognition, a classic of the dynamic clustering algorithm, there are more practical. ISODATA algorithm can not only by adjusting the samples to complete the class cluster analysis of samples, and can automatically type of " merger" and " split" in order to get a few more reasonable type of clustering results. Platform: |
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Author:994 |
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Description: C++中的ISODATA算法示例，ISODATA算法是一种基于统计模式识别、经典的动态聚类算法，有较强的实用性。ISODATA算法不仅可以通过调整样本所属类别完成样本的聚类分析，而且可以自动地进行类别的“合并”和“分裂”，从而得到类数比较合理的聚类结果。
-C++ examples of ISODATA algorithm, ISODATA algorithm is based on statistical pattern recognition, a classic of the dynamic clustering algorithm, there are more practical. ISODATA algorithm can not only by adjusting the samples to complete the class cluster analysis of samples, and can automatically type of " merger" and " split" in order to get a few more reasonable type of clustering results. Platform: |
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Author:jiangjihai |
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Description: 多模型预测的WSN动态簇目标跟踪算法Dynamic multi-cluster model to predict the WSN target tracking algorithm-Dynamic multi-cluster model to predict the WSN target tracking algorithm Platform: |
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Author:vip7510 |
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Description: K-means算法是一种动态聚类方法，这种方法先选择若干样本作为聚类的中心，在按某种聚类准则（通常采用最小距离原则）使各种样本向各个中心积聚，从而得到初始的分类，然后，判断分类的合理性，如果不合理，就修改分类，如此反复的修改聚类的迭代运算，直到合理为止。-K-means algorithm is a dynamic clustering method, this method, select the number of samples as a cluster center in the cluster by some criteria (usually the principle of minimum distance) to the centers to the accumulation of a variety of samples, resulting in the initial classification, then determine the classification is reasonable, if unreasonable, to modify the classification, so repeatedly modified clustering iteration, until a reasonable date. Platform: |
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Author:wjj |
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Description: 熟悉c均值算法，通过程序语言实现该算法，比较每个聚类的初始均值不同时，算法结果的差别。理解动态聚类算法的算法思想-Familiar with the c means algorithm, the algorithm realized by programming language, compare the initial mean of each cluster is not the same time, the difference between algorithm results. Dynamic Clustering Algorithm and understand the thinking of Platform: |
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Author:yshg |
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Description: 本例实现的动态聚类中的ISODATA算法，是一种逻辑结构较为复杂的算法，通过样本均值的迭代计算得到聚类中心。-In this case to achieve the dynamic clustering ISODATA algorithm, is a logical structure more complex algorithm, the iterative sample mean calculated cluster center. Platform: |
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Author:lu |
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Description: 义了一个欧氏距离和监督信息相混合的新的最近邻计算函数，从而将K一均值算法很好地应用于半
监督聚类问题。针对K一均值算法初始质心敏感的缺陷，用粒子群算法的搜索空间模拟聚类的欧氏空间，迭代搜
索找到较优的聚类质心，同时提出动态管理种群的策略以提高粒子群算法搜索效率。算法在UCI的多个数据集
上测试都得到了较好的聚类准确率。-Righteousness of a Euclidean distance and supervision of a mixture of new nearest neighbor calculation functions, thus the K-means algorithm applied to the semi-supervised clustering problem. K-means algorithm the initial center of mass-sensitive defect clustering in the search space of the particle swarm algorithm simulation in Euclidean space, an iterative search to find the optimum cluster centroid, and strategies to improve particle swarm optimization to dynamic management of stocks search efficiency. Algorithm on multiple datasets in the UCI tests are a good clustering accuracy. Platform: |
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Description: k-means算法是一种动态聚类算法，基本原理如下[24]：首先预先定义分类数k，并随机或按一定的原则选取k个样品作为初始聚类中心；然后按照就近的原则将其余的样品进行归类，得出一个初始的分类方案，并计算各类别的均值来更新聚类中心；再根据新的聚类中心对样品进行重新分类，反复循环此过程，直到聚类中心收敛为止。-K- means algorithm is a dynamic clustering algorithm, the basic principle of [24] as follows: first of all the predefined class number k, and random or according to certain principle to choose k as a sample as the initial cluster centers Then according to the principle of the nearest to categorize the rest of the samples, it is concluded that an initial classification scheme, and various other calculated mean to update the cluster centers The samples again according to the new clustering center to reclassify, recycling the process, until the clustering center of convergence. Platform: |
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Author:ouyang |
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Description: 包括AHP，因子分析，回归分析，聚类分析，脉冲响应的相关分析算法并检验，DC-DC部分采用定功率单环控制，部分实现了追踪测速迭代松弛算法，用MATLAB实现动态聚类或迭代自组织数据分析，是路径规划的实用方法，一种噪声辅助数据分析方法。- Including AHP, factor analysis, regression analysis, cluster analysis, Related impulse response analysis algorithm and inspection, DC-DC power single-part set-loop control, Partially achieved tracking speed iterative relaxation algorithm, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Is a practical method of path planning, A noise auxiliary data analysis method. Platform: |
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Author:bbzzfvj |
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Description: 主要是根据carmi,septier and godsill在2012年发表的一篇文章的源程序-the algorithms coded in thisfile are proposed in carmi,septier and godsill, The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking Platform: |
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Author:宁巧娇 |
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Description: Wireless sensor networks with mobile sink are ex-pected to increase the flexibility for gathering information in large-scale sensing and detecting environments. Energy saving becomes one of the most important features of the sensor nodes to extend their lifetime in such networks. A novel tree-based power saving scheme is proposed in this paper to reduce the energy consumption in wireless sensor networks with mobile sink. We adopt a dynamic sorting algorithm to create a tree-cluster routing structure for the sensor nodes. The main goal of this scheme is to reduce the data transmission distances of the sensor nodes by employing the tree structure and multi-hop concepts. Based on the location of mobile sink, the distances between the sensor nodes, and the residual energy of each sensor node, the proposed scheme makes an efficient decision for creating the routing structure. The energy consumption is reduced and the lifetime is extended for the sensor nodes by balancing the network load. Platform: |
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Author:wadgiad |
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Search - Dynamic cluster algorithm