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Aim

Theory

Pretest

Procedure

Simulation

Posttest

References

Feedback

Clustering wines using K-Means

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What type of machine learning algorithm is K-Means Clustering?

a: Supervised learning Explanation

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b: Unsupervised learning Explanation

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c: Reinforcement learning Explanation

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d: Semi-supervised learning Explanation

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What does the symbol 'K' represent in the K-Means algorithm?

a: The number of data points Explanation

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b: The number of features Explanation

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c: The number of clusters to be formed Explanation

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d: The number of iterations Explanation

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What is a 'Centroid' in K-Means clustering?

a: The farthest point in a cluster Explanation

Explanation

b: A randomly selected data point Explanation

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c: The average position or 'center of gravity' of a cluster Explanation

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d: The total number of points in a cluster Explanation

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Why is 'Standardization' (Z-Score) important before running K-Means?

a: To reduce the number of data points Explanation

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b: To ensure every feature is measured on the same scale Explanation

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c: To increase the number of clusters Explanation

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d: To make the data points larger Explanation

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What is the 'Initialization' step in the K-Means process?

a: Calculating the final WCSS Explanation

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b: Assigning points to the nearest centroid Explanation

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c: Setting the starting positions of the centroids Explanation

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d: Normalizing the dataset Explanation

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