can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

Related MCQs

• ______can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.
•             can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to
• 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
• which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes
• In which of the following cases will K-Means clustering fail to give good results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes
• 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
• 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
• 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:
• This unsupervised 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.
• Hierarchical clustering is slower than non-hierarchical clustering?