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[Bio-Recognize] good K-means clusters DL : 0: 这次上传的代码是关于K-means clusters的代码，希望能对大家有用。-The uploaded code is about K-means clusters.I hope it can be a help to everyone.
Update : 2008-10-13 Size : 5.14kb Publisher : 李乐
[Internet-Network] heartbeat-1.99.2.tar DL : 0: linux集群服务器软件代码包，已广泛应用于专业linux服务器构架中，是目前开源的集群代码中最优秀的-Linux cluster server software code. it has been widely used professional linux server architecture, which is the open-source code clusters of the best
Update : 2008-10-13 Size : 2.36mb Publisher : 张利
[Other resource] RAC DL : 0: 在linux上单机模拟Oracle 10g RAC集群-aircraft simulated Oracle 10g RAC clusters
Update : 2008-10-13 Size : 2.38kb Publisher : zby
[Other resource] realDBSCAN DL : 0: 二维的DBSCAN聚类算法，输入（x,y）数组，搜索半径Eps，密度搜索参数Minpts。输出: Clusters，每一行代表一个簇，形式为簇的对象对应的原数据集的ID-two-dimensional clustering algorithm, the input (x, y) array, search radius Eps. Minpts density search parameters. Output : Clusters, each firm on behalf of a cluster, in the form of clusters of objects corresponding to the original data set ID
Update : 2008-10-13 Size : 1.61kb Publisher : 胡瑞飞
[Other resource] High.Performance.Linux.Clusters.With.Oscar.Rocks.o DL : 1: High.Performance.Linux.Clusters.With.Oscar.Rocks.openmosix.And.Mpi.2004--介绍linux集群的好书（英文版），希望大家喜欢
Update : 2008-10-13 Size : 1.72mb Publisher : 符钰
[matlab] clusters DL : 1: 协同微粒群算法，用于原子簇的结构优化。协同微粒群算法模型由南非人Frans van den Bergh提出，对高维问题处理的效果要显著优于基本微粒群算法。-synergies PSO algorithm, for the cluster of structural optimization. Synergy PSO algorithm model from South Africa who Frans van den Bergh, for the high-dimensional problems of the effect was significantly better than the basic particle swarm algorithm.
Update : 2025-02-17 Size : 2kb Publisher :
[Bio-Recognize] good K-means clusters DL : 0: 这次上传的代码是关于K-means clusters的代码，希望能对大家有用。-The uploaded code is about K-means clusters.I hope it can be a help to everyone.
Update : 2025-02-17 Size : 5kb Publisher : 李乐
[AI-NN-PR] realDBSCAN DL : 1: 二维的DBSCAN聚类算法，输入（x,y）数组，搜索半径Eps，密度搜索参数Minpts。输出: Clusters，每一行代表一个簇，形式为簇的对象对应的原数据集的ID-two-dimensional clustering algorithm, the input (x, y) array, search radius Eps. Minpts density search parameters. Output : Clusters, each firm on behalf of a cluster, in the form of clusters of objects corresponding to the original data set ID
Update : 2025-02-17 Size : 1kb Publisher : 胡瑞飞
[matlab] Calculatetheprobability DL : 0: calculatePXTheta---Calculate the probability of each pixel being its color conditioned on all of the clusters that were found at the previous (coarser) iteration. -calculatePXTheta-- Calculate the proba bility of each pixel being its color conditione d on all of the clusters that were found at the pre vious (coarser) iteration.
Update : 2025-02-17 Size : 11kb Publisher : 左贤君
[Other] FuzzyClusteringToolbox DL : 0: The Fuzzy Clustering and Data Analysis Toolbox is a collection of Matlab functions. Its propose is to divide a given data set into subsets (called clusters), hard and fuzzy partitioning mean, that these transitions between the subsets are crisp or gradual.-The Fuzzy Clustering and Data Analysis Toolbox is a collection of Matlabfunctions. Its propose is to divide a given data set into subsets (calledclusters), hard and fuzzy partitioning mean, that these transitions betweenthe subsets are crisp or gradual.
Update : 2025-02-17 Size : 1.92mb Publisher : 在式朝
[matlab] clusters DL : 0: 该算法包包含了hierichal，kmeans，em聚类算法，非常实用。-The algorithm package contains hierichal, kmeans, em clustering algorithm is very practical.
Update : 2025-02-17 Size : 24kb Publisher : 项龙江
[Mathimatics-Numerical algorithms] Clustering DL : 0: Determination of number of clusters in K-Means Clustering and Application in color image segmenta
Update : 2025-02-17 Size : 25kb Publisher : liuhong
[Other] High.Performance.Linux.Clusters.With.Oscar.Rocks.o DL : 0: High.Performance.Linux.Clusters.With.Oscar.Rocks.openmosix.And.Mpi.2004--介绍linux集群的好书（英文版），希望大家喜欢-Linux cluster High.Performance.Linux.Clusters.With.Oscar.Rocks.openmosix.And.Mpi.2004 introduce books (in English), I hope everyone likes
Update : 2025-02-17 Size : 1.72mb Publisher : 符钰
[AI-NN-PR] FuzzyClusteringToolbox DL : 0: 使用matlab实现的各种聚类算法,其中包括具体例子进行详细说明。-The purpose of the development of this toolbox was to compile a continuously extensible, standard tool, which is useful for any MATLAB user for one s aim. In Chapter 1 of the downloadable related documentation one can find a theoretical introduction containing the theory of the algorithms, the definition of the validity measures and the tools of visualization, which help to understand the programmed MATLAB files. Chapter 2 deals with the exposition of the files and the description of the particular algorithms, and they are illustrated with simple examples, while in Chapter 3 the whole Toolbox is tested on real data sets during the solution of three clustering problems: comparison and selection of algorithms estimating the optimal number of clusters and examining multidimensional data sets. About the Toolbox The Fuzzy Clustering and Data Analysis Toolbox is a collection of MATLAB functions. The toolbox provides five categories of functions: - Cluste
Update : 2025-02-17 Size : 2.04mb Publisher : JinJun
[matlab] fisher_classify DL : 0: function [clusters,c,F]=fisher_classify(A,B,data) fisher判别法程序输入A、B为已知类别样本的样本-变量矩阵，data为待分类样本输出C为判别系数向量 -function [clusters, c, F] = fisher_classify (A, B, data) fisher discriminant method procedures input A, B for a sample of known types of samples- variable matrix, data to be classified as output samples for discriminant coefficient vector C
Update : 2025-02-17 Size : 1kb Publisher : 王晶
[Algorithm] hartigansSLC_OpenCV DL : 0: hartigans Sequential Leader Clustering Algorithm in terms of OpenCV (ver.1.1) Sequential Leader algorithm: Hartigan, J. A. (1975), Clustering Algorithms. John Wiley and Sons, Inc., New York, NY. 1. Select maximum cluster "radius" 2. Start with zero clusters 3. Add each item to be clustered to: * Closest cluster if distance <= r * New cluster if distance to closest cluster > r 4. Compute new center from members in cluster 5. Empty the clusters (keeping the centers) 6. Return to step 3 (until no changes are made) -hartigans Sequential Leader Clustering Algorithm in terms of OpenCV (ver.1.1) Sequential Leader algorithm: Hartigan, J. A. (1975), Clustering Algorithms. John Wiley and Sons, Inc., New York, NY. 1. Select maximum cluster "radius" 2. Start with zero clusters 3. Add each item to be clustered to: * Closest cluster if distance <= r * New cluster if distance to closest cluster > r 4. Compute new center from members in cluster 5. Empty the clusters (keeping the centers) 6. Return to step 3 (until no changes are made)
Update : 2025-02-17 Size : 53kb Publisher : sas
[AI-NN-PR] fish-mobile-a--clusters DL : 0: 实现人工鱼移动,聚群,追尾,捕食,迭代20次的效果-20 iterations of artificial fish, artificial fish mobile, clusters, rear-end collision, predation
Update : 2025-02-17 Size : 2kb Publisher : sun miao
[Other] ch05_Arrays-and-Clusters DL : 0: ch05_Arrays and Clusters.rar
Update : 2025-02-17 Size : 331kb Publisher : Peter
[Finance-Stock software system] Analysis-of-data-clusters-obtained-byself-organiz DL : 0: In order to reveal new structures in stock market behavior of the companies drawing up Dow Jones index we apply self-organizing maps (SOM) and group method of data handling (GMDH) algorithms. Using SOM techniques we obtain SOM-maps that establish a new relationship in market structure. Analysis of the obtained clusters was made by GMDH.
Update : 2025-02-17 Size : 208kb Publisher : vasyl
[Other] Data-Processing-on-Large-Clusters DL : 0: Abstract: Map-Reduce is a programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. Through a simple interface with two functions, map and reduce, this model facilitates parallel implementation of many real-world tasks such as data processing for search engines and machine learning. However, this model does not directly support processing multiple related heterogeneous datasets. While processing relational data is a common need, this limitation causes dif- ficulties and/or inefficiency when Map-Reduce is applied on relational operations like joins. We improve Map-Reduce into a new model called MapReduce-Merge. It adds to Map-Reduce a Merge phase that can efficiently merge data already partitioned and sorted (or hashed) by map and reduce modules. We also demonstrate that this new model can express relational algebra operators as well as implement several join algorithms.
Update : 2025-02-17 Size : 149kb Publisher : Maddy

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