Data Warehousing and Data Mining20815 marksNumerical: K-means++ centroidsClustering techniques
Using k-means++ algorithm and Euclidean distance, find the initial 3 cluster centroids from A1 = (3, 11), A2 = (3, 6), A3 = (9, 5), A4 = (6, 9), A6 = (7, 5), A7 = (2, 3), A8 = (5, 10). Choose (3, 11) as one of the initial centroids.
Using k-means++ algorithm and Euclidean distance, find the initial 3 cluster centroids from A1 = (3, 11), A2 = (3, 6), A3 = (9, 5), A4 = (6, 9), A6 = (7, 5), A7 = (2, 3), A8 = (5, 10). Choose (3, 11) as one of the initial centroids. [5]
Data points: Point Coordinates -------------------- A1 (3, 11) A2 (3, 6) A3 (9, 5) A4 (6, 9) A6 (7, 5) A7 (2, 3) A8 (5, 10) Number of clusters: $k = 3$ First centroid (given): $C1 = (3, 11)$ 1. First centroid is fixed as $C1 = (3,11)$. 2. For each point compute $D^2$ = squared Euclidean distance ...