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Name of the College : Mahatma Gandhi University
Department : Computer Science and Engineering
Subject Code/Name : MCSSE 106-2/Information Retrieval , Data Mining And Data Warehousing
Sem : I
Website : mgu.ac.in
Document Type : Model Question Paper

Download Model/Sample Question Paper :
I : https://www.pdfquestion.in/uploads/mgu.ac.in/5030-1-MCSSE%20106-2%20%20INFORMATION%20RETRIEVAL%20,%20DATA%20MINING%20AND%20DATA%20WAREHOUSING%20-1.doc
II : https://www.pdfquestion.in/uploads/mgu.ac.in/5030-2-MCSSE%20106-2%20INFORMATION%20RETRIEVAL%20,%20DATA%20MINING%20AND%20DATA%20WAREHOUSING%20-2.doc

MGU Information Retrieval Data Mining Question Paper

M.Tech Degree Examination :
Branch: Computer Science and Engineering
Specialization: Computer Science and Systems Engineering

Related : MGU MCSSE105-3 Advanced Databases M.Tech Model Question Paper : www.pdfquestion.in/5029.html

Model Question Paper – I

First Semester :
MCSSE 106-2 : Information Retrieval , Data Mining And Data Warehousing
(Regular – 2013 Admissions)
Time: Three Hours
Maximum: 100 Marks
1. (a) Discuss Vector Space Model. Why Vector Space Model generally considered a better retrieval model than Boolean model (15 marks)

(b) What is an inverted index and why is it a critical part of IR system.
Draw the Inverted Index that would be built for the document collection
DOC1: new home sales top forecasts
DOC2: home sales rise in july
DOC3: increase in home sales in july
DOC4: july new home sales rise (10 Marks)
OR

2. (a) What is the difference between Boolean retrieval and ranked retrieval? Why are most publicly available search engines using ranked retrieval . (8 marks)
b) Write notes on pseudo relevance feedback and query expansion with an example (8 marks)

c) Can Information Retrieval System be related to a Data Base Management System? Justify your answer with suitable example? Describe in detail about functional overview of an Information Retrieval System (9 marks)

3. a) How do data warehousing and online analytical processing relate to data mining (8 marks)
b)How is data cube different from tables? explain with examples (10 marks)
c) Differentiate between ROLAP, MOLAP and HOLAP servers (7 marks)
OR

4 a)Explain slice, dice, pivot operations on data cube with suitable examples (10 marks)
b) Write notes on the following
i) Meta data
ii) Data Mart
iii) Fact constellations (15 marks)

5 a) Explain supervised and unsupervised learning with an example (9 marks)
b) Explain regression analysis in data mining (8 marks)
c) List and describe five primitives in DMQL for specifying data mining tasks(8 marks)
OR

6 a) How do hierarchical clustering methods work? Describe two types of hierarchical clustering methods (13marks)

b) A database has four transactions. Let min_sup=60% and min_conf=80%
TID date items_bought
T100 10/15/99 {K,A,D,B}
T200 10/15/99 {D,A,C,E,B}
T300 10/19/99 {C,A,B,E}
T400 10/22/99 {B,A,D}
Find all frequent itemsets using Apriori and FP-growth respectively. Compare the efficiency of the two mining processes (12marks)

7a) How do hierarchical clustering methods work. Describe two types of hierarchical clustering methods (15 marks)
.b) Explain rule based classification (10 marks)
OR

8a) State Bayes theorm and discuss how Bayesian classifier works (9 marks)
.b) Give k-means partitioning algorithm (10 marks)
.c) Write notes on outlier analysis (6 marks)

Model Question Paper – II

M.Tech DEGREE EXAMINATION :
MCSSE 106-2
Information Retrieval , Data Mining And Data Warehousing :
(Regular – 2013 Admissions)
Time: 3 Hours
Max Marks: 100
1. a) What is a test collection? Discuss various evaluation measures used in evaluation of IR systems (10 marks)

b) What is the difference between stemming and lemmatization. What are the advantages and disadvantages . (8 marks)
c) Write notes on Intelligent IR (7 marks)
OR

2. a) How to build an inverted index? Explain with an algorithm. (12.5 marks)
b) Determine the similarity of Vector Space Model between a given query and document . . collection.
Q: gold silver truck
D1: shipment of gold damaged in a fire
D2: delivery of silver arrived in a silver truck
D3: shipment of gold arrived in a truck (12.5 marks)

3.a) Discuss the data ware housing approach to heterogeneous database integration. How does it differs from the traditional approach. (10 marks)

b) Suppose that a data warehouse consists of the three dimensions time, doctor, and patient, and the two measures count and charge, where charge is the fee that a doctor charges a patient for a visit.
i) Draw a schema diagram for the above data warehouse and define the schema using DMQL statement.

ii) Starting with the base cuboid [day, doctor, patient] what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2000. (15 marks)
OR

4. a) Discuss the typical OLAP operations with example (15 marks)
b) Discuss how computations can be performed efficiently on data cubes (10 marks)

5. a) Write notes on data discretization. (12 marks)
b) Discuss the need of dimensionality reduction in data mining systems and various strategies for data reduction (13 marks)
OR

6 a)Describe in detail the Apriori algorithm with suitable example (15 marks)
b) Discuss various methods in data cleaning process (10 marks)

7 a)Explain classification by Decision tree induction with example (10 marks)
b) Illustrate how support vector machines can be used for classification of data(8 marks)
c) Discuss in detail the applications of data mining (7 marks)
OR

8 a) Discuss model based clustering methods (9 marks)
b) Explain Grid based methods for clustering (9 marks)
c) Explain lazy learners (7 marks)