Data Warehousing and Mining (DWM) Question bank 2023

Unit I:

1. Differentiate Data Warehouse and Data Mining. Describe KDD Process.
2. Explain Data Mining Functionalities
3. Technologies are used in Data Mining
4. Different Fields where Data Mining is used.
5. What is Outlier Analysis?
6. Describe all the Major challenges or Issues in data mining.

Unit II:

1. Explain Mean, Mode, Median, Variance, Standard Derivation with
   Suitable Example.
2. What is Measuring Data Similarity and Dissimilarity? State Different types of it. Also Explain with example.
   a) Dissimilarity of Numeric Data: Minkowski Distance.
   b) Dissimilarity for Attributes of Mixed Types.
3. Explain Central Tendency of Data.
4. What are Different types of Attributes in Data Mining.
5. Explain How to measure Dispersion of Data.
6. State the different Data Visualization Techniques and Explain :
   a) Icon-Based Visualization Techniques.
   b) Hierarchical Visualization Techniques.
   c) Visualizing Complex Data and Relations.

Unit III:

1. What is Data Cleaning and Basic methods of Data Cleaning?
2. Discuss the Data Normalization Methods in Data Transformation.
   a. min-max normalization
   b. z-score normalization
   c. Normalization by decimal scaling.
3. What are different methods of handling missing values? Explain.
4. Explain What is Binning in Data Mining.
5. Explain Steps involved in Data Preprocessing.
6. What is Data Discretization? Why there is the need of Data
   Discretization. Explain Simple Discretization : Binning Method

Unit IV:

1. What is Data Warehousing? Why there is need of Separate Data
   Warehouse.
2. Difference Between OLTP and OLAP.
3. Explain Indexing OLAP.
4. Explain meta data in Data Warehouse
5. What is Data Cube and OLAP?
6. Explain OLAP Server Architectures: ROLAP versus MOLAP versus HOLAP.

Unit V:

1. What is Data Cube. Types of Data Cube and its computing methods.
2. Explain general Optimization techniques for efficient computation of Data
   Cubes.
3. Explain Cube Materialization.
4. Explain OLAP based Mining on Sampling data.
5. What is Prediction Mining in Cube Space?
6. Explain Multifeature Cubes: Complex Aggregation at Multiple
   Granularities
7. What is Exception-Based, Discovery-Driven Cube Space Exploration.

Unit VI:

1. Explain Market Basket Analysis with example.
2. What is association rule mining
3. What is the purpose of FP Growth
4. What is Frequent item sets and closed item sets
5. Explain the Apriori Algorithm in Frequent Itemset Mining Method
   with example.
6. How to Generate Association Rules from Frequent Itemsets.
7. Explain Mining Closed and Max Pattern with examples.
8. Explain Frequent Itemsets, Closed Itemsets.

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