DATA MINING | UTU QUESTION PAPER

DATA MINING UTU QUESTION PAPER (2021-22)

DATA MINING  UTU QUESTION PAPER 

BCST-604 (B) 

Roll No.

Time: 3 Hours

Even Semester Examination, 2021-22 Course Name: B.TECH

Branch: Computer Science & Engineering/IT Semester: VI Subject: Data Mining

Max Marks: 100

Number of Printed pages: 2

Note: Attempt all questions: All Questions carry equal marks

Q1. Attempt any four parts of the following: (5 x 4 = 20)

(a) Explain mining multilevel association rules from Transactional databases.

(b) Explain the Market Basket Analysis with suitable example.

(c) Explain Attribute Subset Selection method for Data Deduction with examples

(d) Describe classification. Briefly outline the major ideas of Basiyan classification.

(e) Explain the types of data that often occur in Cluster Analysis and briefly explain how to preprocess that data for clustering

Q 2. Attempt any four parts of the following: (5 x 4 = 20)

(a) Distinguish between dimensionality reduction and numerosity reduction

(b) Define KDD process and describe the phases in KDD process.

(c) Define various steps to build data warehouse.

(d) Briefly explain the concept of frequent item set and closed item set.

(e) Describe various types of OLAP. Distinguish between OLAP and OLTP.

Q3. Attempt any two parts of the following: (10 x 2 = 20)

(a) Draw the box plot for the following data set. Also find outliers.

(126, 132, 138, 140, 141, 142, 143, 144, 144, 144, 145, 146, 147, 148, 148, 149, 149, 150, 150, 150, 154, 155, 158, 158)

(b) Explain with diagram star, snowflake and fact constellation schemas. Also discuss their advantages and disadvantages.

(c) Explain 3 tier architecture of data warehouse. Explain the difference between Data Mart 

and

(d) Data Warehouse.

Q4. Attempt any two parts of the following: (10 x 2 = 20)

(a) Explain association rule mining. Explain the Apriori algorithm to find the frequent item sets.

(b) Explain Data, Information and knowledge. Explain data cleaning, data integration and transformation in details.

(c) Write short notes on:

     a. Quartiles

     b. Histograms

     c. Scatter plots

Q5. Attempt any two parts of the following: (10 x 2 = 20)

(a) Explain the density based clustering method based on connected regions with sufficiently high density (DBSCAN)

(b) Explain 3 tier architecture of data warehouse. Explain the difference between Data Mart and Data Warehouse.

(c) What is a Multidimensional Data Model? How we convert tables and spreadsheets to Data Cubes? Convert 2-D tables into 3-D Data cube with an example.