At the end of the first iteration, the K-means clustering algorithm has clustered the given 8...

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At the end of the first iteration, the K-means clustering algorithm has clustered the given 8 observations into the following 3 clusters: C1= {(4, 4), (5,5), (9,9)} C2 = {(0,5), (5,0), (3,0)} C3= {(8,8), (7,7)} (a) In the second iteration, what will be the L₁ distance for observation (8,8) from the centroid of the cluster C1? (Note: The L₁ distance between two points (x1, y₁) and (x2, y2) is defined as d = |x2x₁ + y2 - y₁) At the end of the first iteration, the K-means clustering algorithm has clustered the given 8 observations into the following 3 clusters: C1= {(4, 4), (5,5), (9,9)} C2 = {(0,5), (5,0), (3,0)} C3= {(8,8), (7,7)} (a) In the second iteration, what will be the L₁ distance for observation (8,8) from the centroid of the cluster C1? (Note: The L₁ distance between two points (x1, y₁) and (x2, y2) is defined as d = |x2x₁ + y2 - y₁)

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To calculate the L1 distance also known as Manhattan distance for observation 88 from the ... View the full answer