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Mid. Term Exam. –Data Warehouse & Data Mining 2004 1. Let X = {(1,2,3), (1,2,4), (1,3,5), (2,3,4), (2,3,5), (2,4,5), (3,4,5)}. (a) if X is the set of frequent itemsets (L3), write down the candidate itemsets of length 4 (C4) after pruning. (b) if X is the set of candidate itemsets, hash-bucket size of a leaf = 2, the branch size of a node = 3 and hash function = item_id % 3, show the hash-tree in Apriori. (c) Indicate the candidates to be checked while counting the supports for a transaction = (1, 3, 4, 6) (d) Place X in a LexTree [2%, 4%, 5%, 4%] 2. Given a transaction database as follows. (a) Compute confidence of rule (C) (A,B,E) (b) compute support of (A,C) (B,E) [2%, 3%] A B C D E 1 2 3 4 5 6 7 8 9 10 3. The database in a query system has 80 items. An expert confirms that 48 items belong to “Good” class. Assume that we submit a query to find items having class “Good”. The query system gives us 60 items, 32 items are really “Good” class among the 60 items. (a) Compute recall (b) compute precision? [3%, 2%] 4. Give two examples of concept hierarchy. [5%] 5. We have a video rental database. Each record in the database has three attributes: Date, Customer_name, Videos. For example, On Nov. 19, “Tom” rent “ID4” and “Gone with the wind”. Using this database and show (a) a typical database query (b) a data mining query [2%, 3%] 6. Explain the MAIN difference(s) between classification and clustering. [5%] 7. Compare classification by decision tree technique and by neural network 1 of 2 Mid. Term Exam. –Data Warehouse & Data Mining 2004 technique. [5%] 8. Compute correlation coefficient for X = <1, 4, 6, 15, 17> and Y = <-5, 8, -7, 2, 19>. [5%] 9. Given a data set below, where “play” is the class attribute. (a) What’s the information of the data set? (b) What’s the entropy of attribute outlook? (c) While creating a corresponding decision tree based on ID3, how do you choose the attribute to be used in the root node? (d) Based on CART, what’s the splitting point of Temperature? (if Cool < Mild < Hot) (e) Given a decision tree below, use this data set as test samples and compute the error rate of the tree. [2%, 3%, 2%, 4%, 4%] Outlook Temperature Humidity Wind Play? Sunny Hot High Weak No Sunny Hot High Strong Yes Overcast Hot High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mild High Weak No Rain Mild Normal Weak Yes Sunny Cool Normal Weak Yes Decision Tree for PlayTennis Outlook Sunny Humidity High No Overcast Rain Yes Normal Wind Strong Yes No Weak Yes 10. Describe the following terms. (a) Data Mining (b) KDD (c) OLAP (d) Predictive Data Mining (e) Descriptive Data Mining (f) Outlier (g) Overfitting [5% each] 2 of 2