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Assume we have a 20 dataset consisting of (0, –6), (4, 4), (0,0) , (–5, 2). We wish to do k-means and k-medoids clustering with k = 2. We initialize the cluster centers with (-5, 2), (0, –6). For this small dataset, in choosing between two equally valid exemplars for a cluster in k-medoids, choose them with priority in the order given above (i.e. all other things being equal, you would choose (0, –6) as a center over (-5, 2)).

Assume we have a 20 dataset consisting of (0, –6), (4, 4), (0,0) , (–5, 2). We wish to do k-means and k-medoids clustering with k = 2. We initialize the cluster centers with (-5, 2), (0, –6). For this small dataset, in choosing between two equally valid exemplars for a cluster in k-medoids, choose them with priority in the order given above (i.e. all other things being equal, you would choose (0, –6) as a center over (-5, 2)).

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Assume we have a 2D dataset consisting of (0, –6), (4, 4) , (0, 0) ,(-5, 2). We wish to do k-means and k-medoids clustering with k = 2. We initialize the cluster centers with (-5, 2) , (0, –6). For this small dataset, in choosing between two equally valid exemplars for a cluster in k-medoids, choose them with priority in the order given above (i.e. all other things being equal, you would choose (0, –6) as a center over (-5, 2)). Clustering 3 K-means algorithm with l1 norm Note: For K-means algorithm with l1 norm, you need to use median instead of mean when calculating the centroid. For details, you can check out this Wiki page. Cluster 1 Center: Cluster 1 Members: Cluster 2 Center: Cluster 2 Members:
Transcribed Image Text:Assume we have a 2D dataset consisting of (0, –6), (4, 4) , (0, 0) ,(-5, 2). We wish to do k-means and k-medoids clustering with k = 2. We initialize the cluster centers with (-5, 2) , (0, –6). For this small dataset, in choosing between two equally valid exemplars for a cluster in k-medoids, choose them with priority in the order given above (i.e. all other things being equal, you would choose (0, –6) as a center over (-5, 2)). Clustering 3 K-means algorithm with l1 norm Note: For K-means algorithm with l1 norm, you need to use median instead of mean when calculating the centroid. For details, you can check out this Wiki page. Cluster 1 Center: Cluster 1 Members: Cluster 2 Center: Cluster 2 Members:
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