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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: JAVA实现的聚类中心的计算 算法比较简单 望多多指教 提宝贵意见-JAVA realize the cluster center is relatively simple algorithm for calculating the exhibitions look to the valuable advice
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Size: 2048 |
Author: sunny |
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Description: JAVA实现的KMENAS聚类算法 共有5个JAVA源文件 请各位指教 -JAVA clustering algorithm KMENAS realize a total of five JAVA source files please advise
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Size: 4096 |
Author: sunny |
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Description: fast implementation of Kmeans clustering algorithm
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Size: 3072 |
Author: jwj |
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Description: matlab版本的k-means聚类程序，很简单易懂，适用于matlab和聚类的初学者-matlab version of the k-means clustering procedure is very simple and easy to understand, apply and clustering matlab beginner
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Size: 3072 |
Author: qianwen |
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Description: 压缩文件里有四种图像分割的算法源代码，即阈值法、区域增长法、分裂合并法和K均值法。图片可用于检验。-The rar folder includes four source code of image segmentation,ie.thresholding, region growing, splitting and merging, kmeans. The images are able to be used for evaluation and verification.
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Size: 156672 |
Author: 杨岩 |
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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)
===========================================
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Size: 5120 |
Author: wzy |
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Description: This rar consists of a document which describes about Kmeans algorithm
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Size: 201728 |
Author: vj |
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