NIELIT Question Paper : B Level Course Data Warehousing & Data Mining

Institute : National Institute of Electronics and Information Technology (nielit.gov.in)
Course : B Level Course
Subject Code/Name : BE6-R4/Data Warehousing & Data Mining
Document Type : Old Question Paper
Location : India
Website : nielit.gov.in

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Data Warehousing & Data Mining :

BE6-R4:
NOTE:
Time: 3 Hours
Total Marks: 100
1. a) State differences between predictive modeling and descriptive modeling.

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b) Explain the differences between discrete and continuous data and give three examples of each?
c) What is meant by hierarchical clustering? Explain.
d) What are the basic features and applications of Naïve Bayes algorithm?

e) What are the strengths and weaknesses of Decision trees?
f) What are the issues in data warehousing?
g) Compare and Contrast OLTP and Data Warehousing systems. (7×4)

2. a) Explain the architecture of Data. Also state the features of Data warehouse?
b) State why tree pruning is useful in decision tree induction? What are the approaches for tree pruning?
c) Explain three pre-processing steps required before feeding data into a data warehouse. (6+6+6)

3. a) Explain the K-means clustering algorithm.
b) Explain in detail the FASMI characteristics of OLAP systems? (9+9)

4. a) Explain the Kohonen’s self-organizing Maps? State the applications of Neural Network?
b) Give the difference between hierarchical and non-hierarchical clustering? Consider eight data points with two dimensions x and y as candidate for agglomerative clustering. The data points are P1(1,1), P2(6,7), P3(4,6), P4(5,7), P5(5,2), P6(2,3), P7(1,2), P8(3,1). Perform
Agglomerative Clustering for the above points using Distance function and show all the steps involved. (9+9)

5. a) What are the steps involved in Data Transformation for making data suitable for Mining?
b) What is Data Cleaning? Explain the different data cleaning approaches?
c) How does snowflake schema overcome the disadvantages of star schema? (6+6+6)

6. a) What are support and confidence in association Rule mining? Write the Apriori algorithm for association rule mining.
b) Explain through example as how and why information gain is used to construct a decision tree?
c) Explain how temporal and spatial databases are different from the normal databases? (7+6+5)

7. Write short notes on any three of the followings:
a) Multimedia Databases
b) Semantic Web
c) Collaborative Filtering
d) Hyperlink induced topic search (HITS) algorithm (3×6)

BE6-R4: Data Warehousing And Data Mining – July 2012 :
1. a) What are the indexes supported in a data warehouse?
b) What are the Contextual information and its type in Data warehouse?
c) Compare and contrast OLTP and Data Warehousing Systems.
d) Formally define association rule mining problem.
e) Explain the SEMMA process Model of data mining?
f) What is bagging? How does it improve performance?
g) How does snowflake schema overcome the disadvantages of star schema? (7×4)

2. a) What are the characteristics of a data warehouse?
b) Draw and explain the Data warehouse Model? State the structure of data inside the data warehouse.
c) What are the steps involved in Data Transformation for making data suitable for Mining? (6+6+6)

3. a) Explain the various schema of the data warehouse?
b) Explain the data selection, cleaning, enrichment, coding and analysis of knowledge discovery process? (9+9)

4. a) Consider the following data set shown in below table where each record represents the weather condition and class attributes shows whether people generally play sports in that weather condition or not.
b) Write an algorithm for K-nearest neighbor classification. (12+6)

5. a) How crossover and mutation is performed in Genetic algorithms? Explain with example?
b) Consider the task of clustering people into two clusters based on their heights and weights given in table using K-Means Algorithms? 9*9