10 Questions
What is the most common measure for evaluating K-means clusters?
Sum of Squared Error (SSE)
How is the error calculated for each point in the context of evaluating K-means clusters?
The distance to the nearest cluster
What does SSE stand for in the context of evaluating K-means clusters?
Sum of Squared Error
What does a general trend indicate about SSE as the number of clusters (K) increases in K-means clustering?
SSE tends to decrease
Why is a lower SSE or higher K not always better in K-means clustering?
It can lead to overfitting
What is the benefit of using the technique of 'Multiple Runs' for solving the initial centroids problem in K-means clustering?
It avoids poor initial placements and increases the chance of finding a better clustering solution.
Which approach uses hierarchical clustering to create a dendrogram and then pick initial centroids based on it?
Hierarchical Clustering for Initial Centroids
What is the importance of choosing initial centroids in K-means clustering?
It can significantly impact the final clustering result.
What is the technique that involves starting with a larger number of initial centroids than the final desired number of clusters, and gradually reducing the number of centroids to K by combining them based on proximity or similarity?
Selecting More Than K Initial Centroids
What does the Elbow Method help to determine in K-means clustering?
The optimal balance between increasing K and decreasing SSE.
Learn about evaluating K-means clusters in cluster analysis. Understand how Sum of Squared Error (SSE) is used as a measure, and how it relates to the intra-cluster distance. Explore the calculation method for SSE and its implications.
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