10 Questions
What is a common practice when initializing centroids in K-means Clustering?
Choosing initial centroids randomly
In K-means Clustering, what role do centroids play?
Represent cluster centers
Which of the following is a key component of the partitional clustering approach used in K-means Clustering?
Centroids
What is the significance of distance metrics in K-means Clustering?
Determining closeness of data points to centroids
How are initial centroids typically chosen in K-means Clustering?
Randomly
What is a common characteristic of K-means convergence according to the text?
It happens in the first few iterations
Why is it mentioned that most of the convergence in K-means happens in the first few iterations?
To explain the efficiency of the algorithm
In K-means clustering, why is the choice of initial centroids critical?
It influences the final outcome of the clustering
What is a caution that should be considered when applying K-means clustering?
Being aware of sensitivity to outliers and noise in the data
Which statement best reflects the significance of early convergence in K-means clustering?
It demonstrates the efficiency and effectiveness of K-means
Test your knowledge on the characteristics of K-means clustering, a partitional clustering approach where each cluster is associated with a centroid. Learn about how each point is assigned to the cluster with the closest centroid and the importance of specifying the number of clusters, K, in the algorithm.
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