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[Mathimatics-Numerical algorithms] kmeans-output-result-document

Description: Kmeans algorithm in C++,output a result.txt containing clustering result.-Kmeans algorithm in C, output a result.txt containing clustering res ult.
Platform: | Size: 49407 | Author: middy | Hits:

[Mathimatics-Numerical algorithms] kmeans-output-result-document

Description: Kmeans algorithm in C++,output a result.txt containing clustering result.-Kmeans algorithm in C, output a result.txt containing clustering res ult.
Platform: | Size: 49152 | Author: middy | Hits:

[.net] Kmeans

Description: 在Visual C++.NET2003平台下实现聚类算法中的K-MEANS算法，对随机生成的点进行了聚类。使用单文档结构，并将聚类结果显示出来。-In the Visual C++. NET2003 platform clustering algorithm to achieve the K-MEANS algorithm, on randomly generated points in the cluster. Use a single document structure, and clustering results are displayed.
Platform: | Size: 63488 | Author: 杨维斌 | Hits:

[AI-NN-PR] KMeans

Description: 可以导入要聚类的文件，分类后可以把结果写入一个文件中-Can be imported to the document clustering, classification results can write a document
Platform: | Size: 1024 | Author: yf | Hits:

[Mathimatics-Numerical algorithms] KMEANS

Description: 改文件是用C++来实现kmeans的算法，学过模式识别的人都知道这个算法的，是用来实现数据的聚类-Is used to document C++ to achieve kmeans algorithms, pattern recognition to learn the people know this algorithm is used for data clustering
Platform: | Size: 29696 | Author: WGT | Hits:

[AI-NN-PR] EvaluateMetric

Description: Clustering Evaluation: Evaluate the clustering result by accuracy and normalized mutual information Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source bestMap hungarian MutualInfo =========================================== fea = rand(50,70) gnd = [ones(10,1) ones(15,1)*2 ones(10,1)*3 ones(15,1)*4] res = kmeans(fea,4) res = bestMap(gnd,res) ============= evaluate AC: accuracy ============== AC = length(find(gnd == res))/length(gnd) ============= evaluate MIhat: nomalized mutual information ================= MIhat = MutualInfo(gnd,res) -Clustering Evaluation: Evaluate the clustering result by accuracy and normalized mutual information Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source bestMap hungarian MutualInfo =========================================== fea = rand(50,70) gnd = [ones(10,1) ones(15,1)*2 ones(10,1)*3 ones(15,1)*4] res = kmeans(fea,4) res = bestMap(gnd,res) ============= evaluate AC: accuracy ============== AC = length(find(gnd == res))/length(gnd) ============= evaluate MIhat: nomalized mutual information ================= MIhat = MutualInfo(gnd,res) ===========================================
Platform: | Size: 5120 | Author: wzy | Hits:

[Software Engineering] Kmeans

Description: K-means clustering implementation document
Platform: | Size: 1742848 | Author: Priya | Hits:

[matlab] Kmeans

Description: K-means K均值聚类算法，不是用工具箱编的，对随机产生的数据进行聚类。压缩文件包括m函数、包含主程序和子函数的word文档。-K-means clustering algorithm, not with the toolbox series of randomly generated data clustering.M functions including compressed files, containing the main program and subroutines word document.
Platform: | Size: 49152 | Author: 晶晶 | Hits:

[Algorithm] kmeans-al-math

Description: C# to achieve k means clustering algorithm, document clustering C# source code, k-means clustering algorithm 29-C# to achieve k means clustering algorithm, document clustering C# source code, k-means clustering algorithm 29111
Platform: | Size: 3072 | Author: mjay90 | Hits:

[JSP/Java] Kmeans

Description: 算法思想：提取文档的TF/IDF权重，然后用余弦定理计算两个多维向量的距离来计算两篇文档的相似度，用标准的k-means算法就可以实现文本聚类。源码为java实现(Algorithm idea: extract the TF/IDF weight of the document, then calculate the distance between two multidimensional vectors by cosine theorem, calculate the similarity of the two documents, and achieve the text clustering with the standard k-means algorithm. Source code for Java implementation)
Platform: | Size: 15360 | Author: startrek | Hits:

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