Assignment Questions

       

Assignment Questions

Data warehousing and Data Mining

Unit – I

Level – 1 (High)

1.      List and explain the different schemas that can be built using dimension tables and fact tables.

2.      What are the major distinguishing features between OLTP and OLAP systems?

Level – 2 (Medium)

3.      What are the different OLAP operations on multi-dimensional data?

4.      Briefly explain different schemas of data ware house?

Level – 3 (Low)

5.      What are the different types of OLAP servers give example of each?

6.      Explain about the three – tier data warehouse architecture.

Unit – II

Level – I (High)

1.      What are the issues in measurement and data collection with respect to data quality?

2.      Explain about summary statistics with an example.

Level – 2 (Medium)

3.      What is a dataset? Explain different types of datasets in detail.

4.      What is an attribute? Explain different attribute types in detail.

  Level – 3 (Low)

5.      What are the challenges that motivated the development of data mining?

6.      Describe the data mining tasks in detail

Unit – III

Level – 1 (High)

1.      Explain the different techniques that are used to handle noisy data.

2.      Explain the various methods that are used in Discretization and concept Hierarchy Generation for numerical data.

Level – 2 (Medium)

3.      What is data integration? Discuss the issues to be considered for data integration.

4.      Explain the various data reduction techniques give advantages of each.

 Level – 3 (Low)

5.      What is the need for Data pre-processing? Explain various techniques.

6.      What is lossless and lossy dimensionality reduction.

Unit – IV

Level – I (High)

1.      Explain the general approach to solving a classification problem.

2.      Discuss the methods that are commonly used to evaluate the performance of a classifier.

Level – 2 (Medium)

3.      Explain the important characteristics of decision tree induction algorithms.

4.      What is meant by i) Model Underfitting ii) Model Overfitting?. Compare them.

  Level – 3 (Low)

5.      Explain Hunt’s algorithm for building a Decision Tree.

6.      Explain the different attribute types that are used in attribute test condition in the Decision Tree.

Unit – V

Level – 1 (High)

1.      Explain the FP-Tree representation. Also explain how the frequent itemset is generated using FP-growth algorithm.

2.      Explain why we use support and confidence in association analysis.

Level – 2 (Medium)

3.      Explain about i) Maximal Frequent Itemsets ii) Closed Frequent Itemsets.

4. Write an algorithm for finding frequent itemsets using candidate generation.

 Level – 3 (Low)

5.      Give the formal definitions of the support and confidence metrics in Association Analysis.

6.      State Apriori Principle. Explain the apriori algorithm with an example.

Unit – VI

Level – 1 (High)

1.      Write the basic Agglomerative Hierarchical Clustering algorithm

2.      Write the basic k-means algorithm. Mention the time and space complexity for the basic k-means algorithm

Level – 2 (Medium)

3.      Write the bisecting k-means algorithm with an example.

4. Briefly describe the strengths and weaknesses of k-means clustering algorithm.

Level – 3 (Low)

5.      Explain the DBSCAN algorithm in detail.

6.      Explain the different types of clusterings.