JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III Year DATA WAREHOUSING AND DATA MINING Previous Question Paper (January/February - 2023 - R18)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. In Part A, Answer all questions. iii) In Part B, Answer any one question from each unit. Each question carries 10 marks and may have a, b as sub questions.
Part A
Explain the differences between star and snowflake schema.
Answer
A star schema features a central fact table connected to denormalized dimension tables, resulting in a simple structure with fewer joins. A snowflake schema is a more complex, normalized version where dimension tables are decomposed into multiple related tables to reduce redundancy.
What is data warehouse metadata?
Answer
Data warehouse metadata is data about the data in the warehouse. It acts as a directory or guide, describing the structure, source, transformation rules, and usage of the data. It is categorized into technical metadata (database schema, mappings) and business metadata (business definitions, report templates).
What are the application areas of data mining?
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What is descriptive and predictive data mining?
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Explain whether association rule mining is supervised or unsupervised type of learning.
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Define Confidence measure.
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What is Decision tree?
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What are the advantage of Bayesian classification?
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What is outlier detection?
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What is the goal of clustering?
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Part B
List out the OLAP operations in multidimensional data model and explain with example.
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Explain multidimensional data model with a neat diagram.
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Compare OLTP and OLAP.
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Explain the types of OLAP server in detail.
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What are the key issues in data Mining? Explain.
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Explain different data mining tasks.
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Describe different data cleaning approaches.
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How can we handle missing values? Explain in detail.
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Write and explain the algorithm for mining frequent item sets without candidate generation. Give relevant example.
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Apply the Apriori algorithm for discovering frequent item sets of the following. Use 3 for minimum support count.
| TID | Items purchased |
|---|---|
| 101 | milk,bread,eggs |
| 102 | milk,juice |
| 103 | juice,butter |
| 104 | milk,bread,eggs |
| 105 | coffee,eggs |
| 106 | coffee |
| 107 | coffee,juice |
| 108 | milk,bread,cookies,eggs |
| 109 | cookies,butter |
| 110 | milk,bread |
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Explain classification by Decision tree induction algorithm with example.
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Explain briefly the test conditions for different types of attributes.
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Explain k- nearest neighbor classifier with example.
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Define Clustering. Explain about Types of Data in Cluster Analysis.
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Classify various Clustering methods.
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Describe in detail about hierarchical clustering methods.
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