Data Mining and Business Intelligence (DMBI) Important Questions

Data Mining and Business Intelligence (DMBI) Important Questions

This IMP’s is contributed by Darshan and Mangesh. Make sure to follow them on their social handles:

• Mangesh Pangam:
  • LinkedIn: Mangesh Pangam
  • Instagram: @Mangesh_2704
• Darshan
• Instagram : darshil2599

Module 1 – Data Warehouse (DWH) Fundamentals with Introduction to Data Mining (Weightage: 5 – 10 marks)

1. Explain the KDD Process with a Diagram.
2. What is data mining? Explain the KDD Process with a Diagram.
3. What are the major issues in data mining.
4. Compare and contrast between OLTP and OLAP.
5. Compare star schema, Snowflakes schema, and star constellation.

Module 2 – Data Exploration and Data Preprocessing (Weightage: 10 – 15 marks)

1. What are various types of attributes?
2. Explain Data Cleaning, Data Integration, Data Reduction.
3. Write a short note on Data Transformation.
4. Define and explain the statistical description of data.
5. State the Apriori Algorithm. Any numerical on the Apriori Algorithm.
6. Explain the concepts: Normalization, Binning, Histogram Analysis.
7. What is noisy data? How to handle noisy data?

Module 3 – Classification (Weightage: 15 – 25 marks)

1. Explain Regression. Explain Linear Regression with an example.
2. Explain Cross-Validation.
3. Explain the concept of Decision Tree Induction.
4. Numerical on Naive Bayes Algorithm: Using the given training dataset classify the following tuple using Naïve Bayes Algorithm.
5. Compare star schema, Snowflakes schema, and star constellation.

Module 4 – Clustering and Outlier Detection (Weightage: 15 – 20 marks)

1. What is an outlier? Describe methods that are used for outlier analysis?
2. Explain K means algorithm in detail. Apply K-means Algorithm to divide the given set of values {2,3,6,8,9,12,15,18,22} into 3 clusters.
3. Explain DBSCAN algorithm with an example.

Module 5 – Frequent Pattern Mining (Weightage: 15 – 20 marks)

1. What are Multiple Levels and Multidimensional Association Rules? Explain with suitable examples for each.
2. Explain the Market Basket Analysis.
3. Explain frequent itemset using candidate generation.
4. Write a short note on Constraint-Based Association Mining.
5. Explain concept of Mining Frequent itemset using vertical data form.
6. Discuss the generation Association Rules from Frequent itemset.
7. Explain briefly Frequent Itemset, Closed Itemset & Association Rules.

Module 6 – Business Intelligence (Weightage: 5 – 10 marks)

1. Explain the Business Intelligence issues.

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