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gulaghasi
2 days ago
14

Suppose that the data mining task is to cluster points (with (x, y) representing location) into three clusters, where the points

are A1(4, 8), A2(2, 4), A3(1, 7), B1(5, 4), B2(5, 7), B3(6, 6), C1(3, 7), C2(7, 8). The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1as the center of each cluster, respectively. Please use the k-means algorithmand show your R code.(a)the three cluster centers after the first round of execution. (b)the final points in the three clusters after the algorithm stops.

Computers and Technology
1 answer:
8_murik_8 [887]2 days ago
5 0
The K-means algorithm operates by taking in a set number of predefined clusters along with data points. It randomly selects initial centers for these clusters. The primary steps to implement the process are: Step 1: Calculate the distance of each data point from all centers. Step 2: Assign each data point to the nearest cluster center. Step 3: After assigning all points, determine the center of the cluster by averaging the data points within that cluster. Repeat these processes until the centers stabilize between iterations. In this example, the centers will be designated as X1 = A1, X2 = B1, and X3 = C1, where A1, B1, and C1 receive assignments according to their nearest cluster.
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