Given the six data points below, perform hierarchical agglomerative clustering using MIN intercluster similarity. Use Manhattan distance to measure the dissimilarity between each two poin

Remember that Manhattan distance is calculated using

${\displaystyle \underset{i=1}{\overset{n}{}}}|p_i-q_i|$

$($a$)$ Show the proximity matrix for following points $(5$ points$)$

$($b$)$ Compute the centroids for $k=4$ clusters steps generated as part of the algorithm by showing each step and its corresponding matrix $(10$ points$)$

$p1=(1,5)$

$p2=(-4,3)$

$p3=(-4,-3)$

$p4=(3,2)$

$p5=(4,1)$

$p6=(-2,-2)$