COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Yes
|
|
|
No
|
|
|
Either A or B
|
|
|
None of the above
|
Detailed explanation-1: -While K-means shall always converge to at least a local minima (in sufficient number of iterations), its convergence to a global minima is not guaranteed. Consider the following example: In the below figure we have a set of blue points on the left side, and a set of red points on the right side.
Detailed explanation-2: -The algorithm does not guarantee convergence to the global optimum. The result may depend on the initial clusters. As the algorithm is usually fast, it is common to run it multiple times with different starting conditions.
Detailed explanation-3: -(True/False) k-means always converges to a local optimum. ANSWER = TRUE. ‘The algorithm always converges (by-definition) but not necessarily to global optimum. ‘
Detailed explanation-4: -To prove convergence of the K-means algorithm, we show that the loss function is guaranteed to decrease monotonically in each iteration until convergence for the assignment step and for the refitting step.
Detailed explanation-5: -K-Means will always give the same results regardless of the initialization of the centroids. For some datasets, the “right” or “correct” value of K (the number of clusters) can be ambiguous, and hard even for a human expert looking carefully at the data to decide.