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

Related MCQs

• his clustering approach initially assumes that each data instance represents a single cluster.
• What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?
• 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.
• Hierarchical clustering is slower than non-hierarchical 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
• Which of the following method is used to find the optimal features for cluster analysis
• True or False: Ensembles will yield bad results when there is significant diversity among the models. Note: All individual models have meaningful and good predictions.
•            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.
• Supervised learning and unsupervised clustering both require which is correct according to the statement.