COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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1 and 2
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2 and 3
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1, 2, and 3
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1 and 3
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Detailed explanation-1: -In which of the following cases will K-Means clustering fail to give good results? The K-Means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different, and the data points follow non-convex shapes.
Detailed explanation-2: -Kmeans assumes spherical shapes of clusters (with radius equal to the distance between the centroid and the furthest data point) and doesn’t work well when clusters are in different shapes such as elliptical clusters.
Detailed explanation-3: -K-Means Clustering is an unsupervised learning algorithm that is used to solve clustering problems in machine learning or data science.
Detailed explanation-4: -Which of the following algorithm is most sensitive to outliers? Out of all the options, K-Means clustering algorithm is most sensitive to outliers as it uses the mean of cluster data points to find the cluster center.
Detailed explanation-5: -K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy.