Homework 6 CS 803, due: April 5th
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Implement the Normalized Cut algorithm from J. Shi and J. Malik
to solve the following segmentation problem.
Generate a synthetic data set to test your code, as follows. Your data
should consist of two classes of 25 points each. Each class of data
points should be drawn from a 2-D Gaussian distribution with random
mean and variance. Each data point will be considered as a node in the
graph. Use the Euclidean distance between nodes as the edge cost. Plot
the data points from each class with different plot symbols (for
example o and x). After running your segmentation code, visualize the
results by coloring each data point with the color associated with the
segmentation results. Repeat for three and four clusters of points
(you may want to put some constraints on the pairwise distance between
the clusters so that the segmentation algorithm will return reasonable
results). Discuss how you determined the number of clusters.
Submit hardcopies (paper copies) of your code and results of the plots
and email me a tar file with all of your code. Also, submit a short
report that describes your code and the results. Homework is due on
April 5th (late homeworks will not be accepted).