You are performing clustering by using the K-means algorithm.
You need to define the possible termination conditions.
Which three conditions can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A . Centroids do not change between iterations.
B . The residual sum of squares (RSS) rises above a threshold.
C . The residual sum of squares (RSS) falls below a threshold.
D . A fixed number of iterations is executed.
E . The sum of distances between centroids reaches a maximum.
Answer: ACD
Explanation:
AD: The algorithm terminates when the centroids stabilize or when a specified number of iterations are completed.
C: A measure of how well the centroids represent the members of their clusters is the residual sum of squares or RSS, the squared distance of each vector from its centroid summed over all vectors. RSS is the objective function and our goal is to minimize it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/k-means-clustering
https://nlp.stanford.edu/IR-book/html/htmledition/k-means-1.html