COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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K-means clustering algorithm
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K-medians clustering algorithm
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K-modes clustering algorithm
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None of the above
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Detailed explanation-1: -k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers.
Detailed explanation-2: -The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. K-medoids clustering is a variant of K-means that is more robust to noises and outliers.
Detailed explanation-3: -It is known that k-means clustering is highly sensitive to the isolated points (called outliers). Such outliers can significantly influence the final cluster configuration and should be removed to obtain quality solutions.
Detailed explanation-4: -The k-medians variation for robust outcomes By using the median instead of the mean, and using a more robust dissimilarity metric, it is much less sensitive to outliers.
Detailed explanation-5: -Expert-Verified Answer K-means clustering algorithm is sensitive to outliers because a means is easily influenced by extreme values.