Q. What is essential for understanding and utilizing the clusters formed by the data mining algorithm?

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Q. In the context of data mining, what is K in the K-means clustering algorithm?

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Q. How does the K-means clustering algorithm initially choose centroids?

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Q. What determines the closeness of a customer record to a centroid in the K-means clustering algorithm?

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Q. How does the algorithm redraw cluster boundaries in the K-means clustering algorithm?

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Q. What is the primary application of decision trees in data mining?

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Q. Why might decision trees be suitable in cases where the reasons for predictions are important?

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Q. How is the effectiveness of a decision tree measured?

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Q. What does pruning aim to achieve in decision trees?

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Q. In memory-based reasoning (MBR), what does the algorithm use to predict unknown instances?

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Q. What does the distance function do in MBR?

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Q. What is the key role of the combination function in MBR?

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Q. In MBR, what does the nearest neighbor predict for unknown respondents?

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Q. What are the three critical issues when solving a data mining problem using MBR?

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Q. What is the primary focus of the link analysis algorithm?

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Q. What is the purpose of associations discovery in link analysis?

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Q. What are association rules in link analysis?

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Q. What is the challenge associated with scaling in associations discovery?

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Q. In sequential pattern discovery, what role does the time element play?

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Q. What is the basic unit of an artificial neural network?

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