What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?

Related MCQs

• Hierarchical clustering is slower than non-hierarchical clustering?
• What is true about K-Mean Clustering? 1. K-means is extremely sensitive to cluster center initializations 2. Bad initialization can lead to Poor convergence speed 3. Bad initialization can lead to bad overall clustering
• To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible
• What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
• 8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0),(2,5)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration?
• This clustering algorithm initially assumes that each data instance represents a single cluster.
• This  clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration Select one:
• Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering? 1. Single-link 2. Complete-link 3. Average-link
• Hierarchical agglomerative clustering is typically visualized as?
• This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration