K-means Clustering and Principal Component Analysis

Introduction

• Implement the K-means clustering algorithm and apply it to compress an image.

Files included in this exercise

• ex7.m - Octave/MATLAB script for the rst exercise on K-means
• ex7 pca.m - Octave/MATLAB script for the second exercise on PCA
• ex7data1.mat - Example Dataset for PCA
• ex7data2.mat - Example Dataset for K-means
• ex7faces.mat - Faces Dataset
• bird small.png - Example Image
• displayData.m - Displays 2D data stored in a matrix
• drawLine.m - Draws a line over an exsiting gure
• plotDataPoints.m - Initialization for K-means centroids
• plotProgresskMeans.m - Plots each step of K-means as it proceeds
• runkMeans.m - Runs the K-means algorithm
• submit.m - Submission script that sends your solutions to our servers
• [?] pca.m - Perform principal component analysis
• [?] projectData.m - Projects a data set into a lower dimensional space
• [?] recoverData.m - Recovers the original data from the projection
• [?] findClosestCentroids.m - Find closest centroids (used in K-means)
• [?] computeCentroids.m - Compute centroid means (used in K-means)
• [?] kMeansInitCentroids.m - Initialization for K-means centroids
• ? indicates files you will need to complete

IDE : Octave_4.4.0

ex7.vol1.egg

ex7.vol2.egg