Perform the following tasks in "R Programming" :
> We would like to use points in a 3-dimensional space as data to cluster. You are expected to generate two data sets:
* The first data set (D1) should contain 1,000,000 3D points whose coordinates are random numbers generated using a uniform distribution. Save D1 in a file.
* The second data set (D2) should contain 1,000,000 3D points; you should generate 40 numbers using a uniform random number generator - these numbers should be used as the parameters for 20 normal distributions (mean and stdev). Rotating cyclically, use these normal distributions to generate the coordinates of the 1,000,000 points. Save D2 in a file.
> Implement the CURE algorithm discussed in class and in the textbook. Use traditional euclidean distance as the distance measure. Your implementation should write the clusters in separate files. You can make reasonable assumptions to fill the gaps in the description of CURE. Your implementation should print on the screen statistical information about the clusters produced (# of clusters, position of the representatives, number of points in the cluster).
Make sure to provide a detailed report and analysis of your code and its execution on the two data sets.
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