Q. Why is a clean and complete data warehouse considered the bedrock for data mining?

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Q. What role does parallel processing technology play in the data warehouse environment?

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Q. How does data mining differ from traditional analysis in terms of data usage from the data warehouse?

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Q. What compromise approach is suggested for the level of granularity in the data warehouse for data mining engagements?

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Q. What is the relationship between data mining algorithms and data mining techniques?

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Q. How are data mining processes related to data mining applications?

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Q. What is clustering in the context of data mining?

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Q. How is cluster detection characterized in terms of knowledge discovery?

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Q. In cluster detection, what is the role of variables like age group and income level?

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Q. What is the purpose of forming clusters in data mining?

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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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