关注次数： 382
下载次数： 2
文件大小： 33KB
中文说明：
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.
English Description：
K-means algorithm, does not require new prediction of the results after the establishment of the model, there is no corresponding label, just clustering the data according to the characteristics of the data. Principal component analysis (PCA) is used to reduce the dimension of features
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