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ITS531-Business Intelligence

Question

Answered

Assignment Purpose

To understand how k-means clustering algorithm works

To understand and compare PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) dimensionality reduction methods

To understand how knn classification algorithm works

Questions:

1.Implement the k-means clustering algorithm yourself in python. Cluster the training set k=10 and k=20 clusters. Run your clustering algorithm 10 times and report the reconstruction errors on training and test datasets for each run and for each value of k=10 and k=20 (number of clusters). 

2.Implement PCA (Principal Component Analysis) algorithm yourself. Reduce the dimensionality of the training set by means of computing the parameters required by PCA on the training set. Display the training set using the first two projected dimensions of the dataset and compare with the plot in the textbook.

3.Implement LDA (Linear Discriminant Analysis) algorithm yourself. Reduce the dimensionality of the training set by means of computing the parameters required by LDA on the training set. Display the training set using the first two projected dimensions of the dataset and compare with the plot in the textbook.

4.(use a library) Use knn (k nearest neighbor) classification algorithm with k=3, 5, 15 to predict the class labels (0, 1, 2, …, 9) for the original test set (in 64 dimensions) using the original training set as the training dataset. Compute the confusion matrix and also the test error for each class (0, 1, 2, ….,9) on the test dataset. 

5.Repeat Q4) but use 

6.PCA projected test set (PCA parameters computed on the training set) in 2, 5, 9 dimensions. 

7.LDA projected test set (LDA parameters computed on the training set) in 2, 5, 9 dimensions. 

8.Does using PCA or LDA help with the test error of the knn? If so, for which classes does it help most? 

ITS531-Business Intelligence

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