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3 | PSN COLLEGE OF ENGINEERING AND TECHNOLOGY (Autonomous), Melathediyoor, Tirunelveli – 627152 | |||||||||||||
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5 | END SEMESTER - QUESTION BANK / AY (2023-2024) / Even | |||||||||||||
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7 | Branch: B.E CSE | Department of Computer Science and Engineering | Year / Semester : III / VI | |||||||||||
8 | Course Code: 503018 | Course Name: Data Warehousing and Data Mining | ||||||||||||
9 | Date : | Session: | Max. Marks: 100 Marks | |||||||||||
10 | Time : 3.00 Hours | Regulation: 2018 | ||||||||||||
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12 | Course Outcomes (COs): At the end of the course, the student will be able to | |||||||||||||
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14 | CO1: | Understand the data warehouse and OLAP technology | ||||||||||||
15 | CO2: | Apply various data preprocessing techniques | ||||||||||||
16 | CO3: | Identify appropriate data mining classification algorithms to solve real world problems | ||||||||||||
17 | CO4: | Identify appropriate data mining clustering algorithms to solve real world problems | ||||||||||||
18 | CO5: | Understand the other data mining methodologies, applications and trends | ||||||||||||
19 | BL – Bloom’s Level (1- Remembering, 2- Understanding, 3 – Applying, 4 – Analysing, 5 – Evaluating, 6 - Creating); CO – Course Outcome; | |||||||||||||
20 | UNIT-I | |||||||||||||
21 | Q. No. | PART-A (2 Marks) | Marks | CO | BL | |||||||||
22 | 1 | How would you describe data warehouse | 2 | CO1 | 1 | |||||||||
23 | 2 | Describe star schema. | 2 | CO1 | 2 | |||||||||
24 | 3 | What is the relationship between Data Warehouse and Database | 2 | CO1 | 4 | |||||||||
25 | 4 | Illustrate Snowflake Schema. | 2 | CO1 | 2 | |||||||||
26 | 5 | Demonstrate the main idea of ETL ? | 2 | CO1 | 2 | |||||||||
27 | 6 | How would you compare dimension table and fact table | 2 | CO1 | 1 | |||||||||
28 | 7 | How would you compare OLTP and OLAP? | 2 | CO1 | 2 | |||||||||
29 | 8 | Summerize the Characteristics of OLAP | 2 | CO1 | 2 | |||||||||
30 | 9 | Illustrate multidimensional data model. | 2 | CO1 | 2 | |||||||||
31 | 10 | How would you classify OLAP | 2 | CO1 | 4 | |||||||||
32 | 11 | Which statements support Data mining. | 2 | CO1 | 2 | |||||||||
33 | 12 | How would you describe Data Cube | 2 | CO1 | 2 | |||||||||
34 | 13 | Define data mart. | 2 | CO1 | 2 | |||||||||
35 | 14 | Differentiate between Data Warehouse versus Operational DBMS | 2 | CO1 | 2 | |||||||||
36 | 15 | List some properties of data Marts. | 2 | CO1 | 2 | |||||||||
37 | Q. No. | PART – B ( 16 MARKS) | Marks | CO | BL | |||||||||
38 | 1 | Explai the following (i) Design of data warehousing. (ii)Demonstrate multidimensional data model with a neat diagram. | 16 | CO1 | 3 | |||||||||
39 | 2 | Briefly explain the following (i) Classify the Techniques in Data Transformation (ii) Describe Binning and Apply Binning Techniques to Solve a Problem | 16 | CO1 | 4 | |||||||||
40 | 3 | Demonstrate the List of OLAP operations and explain it with an example. | 16 | CO1 | 2 | |||||||||
41 | 4 | Outline the Architecture of data warehouse | 16 | CO1 | 2 | |||||||||
42 | 5 | Illustrate the Various Process of ETL? | 16 | CO1 | 2 | |||||||||
43 | 6 | How OLAP is Superior to OLTP | 16 | CO1 | 4 | |||||||||
44 | 7 | Discuss about various schemas of a data warehouse. | 16 | CO1 | 2 | |||||||||
45 | 8 | Explain in detail about the implementation of a data warehousing. | 16 | CO1 | 2 | |||||||||
46 | UNIT-II | |||||||||||||
47 | Q. No. | PART-A ( 2 Marks) | Marks | CO | BL | |||||||||
48 | 1 | Describe data preprocessing techniques. | 2 | CO2 | 2 | |||||||||
49 | 2 | List out the preprocessing techniques available in data mining. | 2 | CO2 | 4 | |||||||||
50 | 3 | Why Is data cleaning so important ? | 2 | CO2 | 2 | |||||||||
51 | 4 | What are the smoothing techniques available to remove noise | 2 | CO2 | 1 | |||||||||
52 | 5 | Illustrate data transformation | 2 | CO2 | 2 | |||||||||
53 | 6 | Give some Benefits of data reduction. | 2 | CO2 | 2 | |||||||||
54 | 7 | Illustrate Data Pre-Processing | 2 | CO2 | 2 | |||||||||
55 | 8 | List the Terms included in data mining task primitives? | 2 | CO2 | 2 | |||||||||
56 | 9 | What is generalization? | 2 | CO2 | 2 | |||||||||
57 | 10 | What is summarization? | 2 | CO2 | 4 | |||||||||
58 | 11 | Define discretization. | 2 | CO2 | 2 | |||||||||
59 | 12 | Define transactional databases. | 2 | CO2 | 2 | |||||||||
60 | 13 | Define relational databases. | 2 | CO2 | 2 | |||||||||
61 | 14 | Differentiate the two types of regression. | 2 | CO2 | 2 | |||||||||
62 | 15 | Define a concept hierarchy. | 2 | CO2 | 2 | |||||||||
63 | Q. No. | PART – B ( 16 MARKS) | Marks | CO | BL | |||||||||
64 | 1 | Classify the techniques available in Data Cleaning | 16 | CO2 | 2 | |||||||||
65 | 2 | Divide the Data Reduction steps and Explain about it. | 16 | CO2 | 4 | |||||||||
66 | 3 | Discuss data discretization and concept hierarchy generation | 16 | CO2 | 3 | |||||||||
67 | 4 | Briefly discuss about Data Smoothing | 16 | CO2 | 3 | |||||||||
68 | 5 | Explain the data preprocessing techniques in detail. | 16 | CO2 | 2 | |||||||||
69 | 6 | Categories the data transformation in detail. | 16 | CO2 | 4 | |||||||||
70 | 7 | Briefly discus about the detail of data reduction | 16 | CO2 | 2 | |||||||||
71 | 8 | Discuss about Data Integration. | 16 | CO2 | 2 | |||||||||
72 | UNIT-III | |||||||||||||
73 | Q. No. | PART-A ( 2 Marks) | Marks | CO | BL | |||||||||
74 | 1 | Categorize the Components of Bayesian Belief Network | 2 | CO3 | 2 | |||||||||
75 | 2 | List the models in Naïve Bayes classification | 2 | CO3 | 4 | |||||||||
76 | 3 | Classify the steps available in Python implementation of the Naïve Bayes algorithm. | 2 | CO3 | 2 | |||||||||
77 | 4 | Give some Advantages of Rule Based Data Mining Classifier. | 2 | CO3 | 1 | |||||||||
78 | 5 | List out the Properties of Rule based classifier | 2 | CO3 | 2 | |||||||||
79 | 6 | Descibe the expression of IF-THEN Rules. | 2 | CO3 | 2 | |||||||||
80 | 7 | Differentiate Pre Pruning and Post Pruning methods | 2 | CO3 | 2 | |||||||||
81 | 8 | What are the Steps available in Naïve Bayes Classifier algorithm to solve the problem. | 2 | CO3 | 2 | |||||||||
82 | 9 | Define Information Gain. | 2 | CO3 | 2 | |||||||||
83 | 10 | Create a Structure for Unprunned Decision Tree. | 2 | CO3 | 4 | |||||||||
84 | 11 | Give the Advantages of Naïve Bayes Classifier algorithm. | 2 | CO3 | 2 | |||||||||
85 | 12 | List the benefits of decision Tree Induction | 2 | CO3 | 2 | |||||||||
86 | 13 | Define data transformation. | 2 | CO3 | 2 | |||||||||
87 | 14 | Define a concept hierarchy. | 2 | CO3 | 2 | |||||||||
88 | 15 | Define data reduction. | 2 | CO3 | 2 | |||||||||
89 | Q. No. | PART – B ( 16 MARKS) | Marks | CO | BL | |||||||||
90 | 1 | Illustrate Naïve Bayesian Classification and explain it with an example | 16 | CO3 | 5 | |||||||||
91 | 2 | Discuss Rule Based Classification Methods. | 16 | CO3 | 4 | |||||||||
92 | 3 | Describe Bayes Classification Methods. | 16 | CO3 | 3 | |||||||||
93 | 4 | Briefly discuss about Bayesian Belief Network. | 16 | CO3 | 3 | |||||||||
94 | 5 | Create a decision tree form training tuples of data partition using an Algorithm | 16 | CO3 | 6 | |||||||||
95 | 6 | Briefly Decribe about Attribute Selection Methods | 16 | CO3 | 4 | |||||||||
96 | 7 | How would you represent the Decision tree | 16 | CO3 | 2 | |||||||||
97 | 8 | Describe Various Pruning methods | 16 | CO3 | 2 | |||||||||
98 | UNIT-IV | |||||||||||||
99 | Q. No. | PART-A ( 2 Marks) | Marks | CO | BL | |||||||||
100 | 1 | What do you go for clustering analysis? | 2 | CO4 | 2 | |||||||||