Given the samples x1 = {1, 0}, x2 = {0, 1}, x3 = {2, 1}, and x4 = {3, 3}, suppose that the samples are randomly clustered into two clusters c1 = {x1, x3} and c2 = {x2, x4}. (a) apply one iteration of the k - means partitional - clustering algorithm, and fi nd a new distribution of samples in clusters. what are the new centroids?
(a) how can you prove that the new distribution of samples is better than the initial one?
(b) what is the change in the total square - error?
(c) apply the second iteration of the k - means algorithm and discuss the changes in clusters.

none of the above.

for one of the equations to be used in the process, there has to be a term in the equation since squared is .

since there is only a term in the first equation, we cannot derive a from completing the square.

solving with completing the square

start with a line segment pq that we will copy. mark a point r that will be one endpoint of the new line segment. set the compasses' point on the point p of the line segment to be copied. adjust the compasses' width to the point q. the compasses' width is now equal to the length of the line segment pq.

step-by-step explanation:

6e14 is the answer.

table a is wierd

step-by-step explanation:

6*f