Podcast
Questions and Answers
What is a common practice when initializing centroids in K-means Clustering?
What is a common practice when initializing centroids in K-means Clustering?
- Assigning centroids in ascending order
- Setting initial centroids at the origin
- Initializing centroids based on the first data point
- Choosing initial centroids randomly (correct)
In K-means Clustering, what role do centroids play?
In K-means Clustering, what role do centroids play?
- Measure cluster similarity
- Define the number of clusters
- Identify outliers in the dataset
- Represent cluster centers (correct)
Which of the following is a key component of the partitional clustering approach used in K-means Clustering?
Which of the following is a key component of the partitional clustering approach used in K-means Clustering?
- Centroids (correct)
- Decision trees
- Confusion matrices
- Association rules
What is the significance of distance metrics in K-means Clustering?
What is the significance of distance metrics in K-means Clustering?
How are initial centroids typically chosen in K-means Clustering?
How are initial centroids typically chosen in K-means Clustering?
What is a common characteristic of K-means convergence according to the text?
What is a common characteristic of K-means convergence according to the text?
Why is it mentioned that most of the convergence in K-means happens in the first few iterations?
Why is it mentioned that most of the convergence in K-means happens in the first few iterations?
In K-means clustering, why is the choice of initial centroids critical?
In K-means clustering, why is the choice of initial centroids critical?
What is a caution that should be considered when applying K-means clustering?
What is a caution that should be considered when applying K-means clustering?
Which statement best reflects the significance of early convergence in K-means clustering?
Which statement best reflects the significance of early convergence in K-means clustering?
