Engineering BE Computer Engineering Sem 6 May 2019 2019 Data Warehouse And Mining Question Paper - Mumbai University | munotes

Questions asked in this paper

1. Q2 Answer any three out of remaining five questions
2. Q3 Assume any suitable data wherever required and justify the same
   • a) spatial data structures? Outline their importance in GIS. 5
   • b) What is Metadata? Why do we need metadata when search engines like Google seem 5
   • c) In real-world data, tuples with missing values for attributes are a common occurrence. Describe various methods for handling this problem 5
   • d) With respect to web mining, is it possible to detect visual objects using meta-objects? 5
3. Q2 a) Suppose that a data warehouse for DB-University consists of the four dimensions — student, course, semester, and instructor, and two measures count and avg-grade. At the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg-grade measure stores the actual course grade of the student. At higher conceptual levels, avg-grade stores the average grade for the given combination 10 marks
   • i. Draw schema diagram for the data warehouse li. Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should you perform in order to list the average grade of CS courses for each DB-University student
   • b) What is the relationship between data warehousing and data replication? Which form of replication (synchronous or asynchronous) is better suited for data warehousing? Why? Explain with appropriate example 10
4. Q3 a) The following table consists of training data from an employee database. The data have been generalized. For example, 35” for age represents the age range of 31 to 35 For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row Let status be the class label attribute 10 marks
   • i. would you modify the basic decision tree algorithm to take into consideration the count of each generalized data tuple (i.e., of each row entry)? Use your algorithm to construct a decision tree from the given data Paper Subject Code: 88903 Data Warehouse and Mining Why is tree pruning useful in decision tree induction? What is a drawback of using [10] separate set of tuples to evaluate pruning? Given a decision tree, you have the option of
   • (i) converting the decision tree to rules and then pruning the resulting rules, or (ii) pruning the decision tree and then converting the pruned tree to rules. What advantage does (i) have over (ii)?
5. Q4 a) Suppose that the data mining task is to cluster points (with (x, y) representing location) into three clusters, where the points are: Aj (2, 10), Az (2, 5), 4), Bi (5, 8), Bo (7, 5), Bs (6, 4), Ci (1, 2), C2 (4, 9) The distance function is Euclidean distance. Suppose initially we assign Ai, Bi, and as the center of each cluster, respectively. Use the k-means algorithm to show only (i) The three cluster centers after the first round of execution (ii) The final three clusters 10 marks
   • b) Briefly outline with example, how to compute the dissimilarity between objects described by the following: 10
   • i. Nominal attributes
6. Q5 Frequent pattern mining algorithms considers only distinct items in a transaction. However, multiple occurrences of an item in the same shopping basket, such as four cakes and three jugs of milk, can be important in transactional data analysis. How can one mine frequent itemsets efficiently considering multiple occurrences of items? Generate Frequent Pattern Tree for the following transaction with 30% minimum 10 marks
   • b) Differentiate between simple linkage, average linkage and complete linkage algorithms. Use complete linkage algorithm to find the clusters from the following dataset 10
   • a) Data quality can be assessed in terms of several issues, including accuracy, completeness, and consistency. For each of the above three issues, discuss how data quality assessment can depend on the intended use of the data, giving examples. Propose two other dimensions of data quality 10
   • b) Present an example where data mining is crucial to the success of a business. What data mining functionalities does this business need (e.g., think of the kinds of patterns that could be mined)? Can such patterns be generated alternatively by data query processing or simple statistical analysis? 10

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