CS 428_quiz_pre

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Santa Clara University *

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101

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Computer Science

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Dec 6, 2023

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docx

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CS 428 1. What is data mining? a) Extracting minerals from the earth b) Extracting useful information from large datasets c) Mining cryptocurrency d) Building data warehouses 2. Which of the following is not a data mining task? a) Classification b) Clustering c) Sorting d) Association rule mining 3. In data mining, what does the term "apriori" refer to? a) A type of algorithm used for clustering b) A measure of data quality c) A frequent itemset generation algorithm for association rule mining d) A method for dimensionality reduction 4. What is the goal of clustering in data mining? a) Predicting a target variable b) Assigning labels to data points c) Discovering natural groupings in data d) Finding association rules 5. Which algorithm is commonly used for association rule mining? a) Decision Tree b) K-Means c) Apriori d) Naive Bayes 6. What is the main difference between supervised and unsupervised learning in the context of data mining? a) Supervised learning requires labeled data, while unsupervised learning does not. b) Unsupervised learning is only used for classification tasks. c) Supervised learning does not involve training a model. d) Unsupervised learning requires a target variable.
7. What is the purpose of the "support" measure in association rule mining? a) It measures the relevance of the association rule. b) It indicates the percentage of transactions that contain all items in the rule. c) It measures the confidence of the rule. d) It determines the number of clusters in the data. 8. Which of the following is an example of a dimensionality reduction technique used in data mining? a) Decision Trees b) Principal Component Analysis (PCA) c) K-Means d) Apriori algorithm 9. What is the concept of overfitting in the context of data mining? a) The model performs well on the training data but poorly on new, unseen data. b) The model is too simple to capture the underlying patterns in the data. c) The model perfectly fits the training data. d) The model is not trained for a sufficient number of epochs. 10. In data mining, what is the "curse of dimensionality"? a) The more features or dimensions in the data, the harder it becomes to analyze. b) A good thing, as it leads to more accurate models. c) The concept of having too few dimensions in the data. d) The measure of data quality. Answers: 1. b 2. c 3. c 4. c 5. c 6. a 7. b 8. b 9. a 10. a
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