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文件名称:K-Means PCA降维
介绍说明－－下载内容来自于网络，使用问题请自行百度
K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the characteristics of data clustering data. The principal component analysis reduces the dimension, carries on the visualization operation to the data, reduces the dimension to the features.)
(系统自动生成,下载前可以参看下载内容)
下载文件列表
Homework3\computeCentroids.m
Homework3\displayData.m
Homework3\drawLine.m
Homework3\ex3.m
Homework3\ex3data1.mat
Homework3\ex3data2.mat
Homework3\ex3_pca.m
Homework3\featureNormalize.m
Homework3\findClosestCentroids.m
Homework3\Homework3.docx
Homework3\kMeansInitCentroids.m
Homework3\pca.m
Homework3\plotDataPoints.m
Homework3\plotProgresskMeans.m
Homework3\projectData.m
Homework3\recoverData.m
Homework3\runkMeans.m
Homework3\~$mework3.docx
Homework3
Homework3\displayData.m
Homework3\drawLine.m
Homework3\ex3.m
Homework3\ex3data1.mat
Homework3\ex3data2.mat
Homework3\ex3_pca.m
Homework3\featureNormalize.m
Homework3\findClosestCentroids.m
Homework3\Homework3.docx
Homework3\kMeansInitCentroids.m
Homework3\pca.m
Homework3\plotDataPoints.m
Homework3\plotProgresskMeans.m
Homework3\projectData.m
Homework3\recoverData.m
Homework3\runkMeans.m
Homework3\~$mework3.docx
Homework3
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