I want to analyze the effectiveness and efficiency of kernel methods for which I would require 3 different data-set in 2 dimensional space for each of the following cases:
- BAD_kmeans: The data set for which the kmeans clustering algorithm will not perform well.
- BAD_pca: The data set for which the Principal Component Analysis (PCA) dimension reduction method upon projection of the original points into 1-dimensional space (i.e., the first eigenvector) will not perform well.
- BAD_svm: The data set for which the linear Support Vector Machine (SVM) supervised classification method using two classes of points (positive and negative) will not perform well.
Which packages can I use in R to generate the random 2d data-set for each of the above cases ? A sample script in R would help in understanding