MCSCS106-1 Data Mining Concepts M.Tech Model Question Paper : mgu.ac.in

Name of the College : Mahatma Gandhi University
Department : Computer Science and Engineering
Subject Code/Name : MCSCS 106-1/DATA MINING CONCEPTS
Sem : I
Website : mgu.ac.in
Document Type : Model Question Paper

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MGU Data Mining Concepts Question Paper

M.TECH. DEGREE EXAMINATION :
Branch: Computer Science and Engineering
Specialization : Computer Science and Engineering

Related : MGU MCSCS105-3 Multicore Architecture M.Tech Model Question Paper : www.pdfquestion.in/5015.html

Model Question Paper – I
First Semester :
MCSCS 106-1 DATA MINING CONCEPTS (Elective II)
(Regular – 2013 Admission onwards)
Time: 3hrs
Maximum:100 marks

Elective I

Answer the following Questions :
1. a) Describe different preprocessing steps in data mining (16)
b) Discuss about constraint based association mining (9)
or
2. a) With suitable example explain the generation of frequent item sets using Apriori algorithm (20)
b) What are the criteria for the classification of frequent pattern mining? (5)

3. a) Explain lazy learners (16)
b) Explain Decision Tree Induction (9)
or
4. a) Explain Associative Classification (16)

b) Explain the issues regarding classification and prediction (9)

5. a) Explain the different steps involved in building a data warehouse. (15)
b) Write a note on multidimensional data model and metadata (10)
or
6. a) Discuss the Categorization of Major Clustering Methods (16)
b) Explain the Clustering of High-Dimensional Data (9)

7. a) Explain briefly Statistical approaches and Proximity based approaches (20)
b) Explain K- medoids clustering(5)
or
8. a) Discuss Web Mining (20)
b) Explain any one of the density based clustering method (5)

Elective II

MCSCS 106-1
DATA MINING CONCEPTS (Elective II)
(Regular – 2013 Admission onwards)
Time: 3hrs
Maximum:100 marks
Answer the following Questions :
1. a) Explain the Apriori algorithm for frequent item set mining with an example (16)
b) Discuss mining of multilevel association rules from transactional databases (9)
or
2. a) Explain various methods of data cleaning in detail (13)
b) Explain the term data discretization (12)

3. a) State Bayes theorem and discus how Bayesian classifier woks (16)
b) Differentiate between classification and prediction (9)
or
4. a) Explain rule based classification (13)
b) Explain the use of ensemble methods in data mining (12)

5. a) Explain the architecture of a data warehouse with a neat diagram (16)
b) Discuss the typical OLAP operations (9)
or
6. a) Explain the multidimensional model of a data warehouse (13)
b) Write notes on reporting and query tools (12)

7. a) Explain K – means clustering with an example(16)
b) Explain K- medoids clustering with a an example (9)
or
8. a) Discuss in detail the applications of data mining(16)
b) Explain any one of the density based clustering method in detail (9)

Syllabus

Module 1 :
Data Mining : – Tasks and Functionalities –Attribute types-Preprocessing –Similarity and Dissimilarity measures
Association Rule Mining : – Efficient and Scalable Frequent Item set Mining Methods – Mining Various Kinds of Association Rules – Correlation Analysis – Mining in Multidimensional space -Constraint- Based Frequent Pattern Mining

Module 2 :
Classification and Prediction :  Issues – Decision Tree Induction – Bayesian Classification – Rule Based Classification – Model Evaluation-Classifier Performance
Advanced Classification : Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Ensemble Methods – Model Section.

Module 3 :
Data Warehousing and Business Analysis : – Data warehousing Components -Mapping the Data Warehouse to a Multiprocessor Architecture – Metadata –OLTP and OLAP
Cluster Analysis : Categorization of Major Clustering Methods – Clustering High-Dimensional Data – Constraint- Based Cluster Analysis-Fuzzy clusters

Module 4 :
Outlier Analysis :- Statistical approaches-Proximity based approaches-Clustering and Classification based approaches Advanced Techniques : -Web Mining, Spatial Mining, Text Mining

References :
1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Elsevier, Reprinted 2008.
2. Pang-Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data Mining”, Pearson Education, 2007

3. K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data mining Theory and Practice”,Easter Economy Edition, Prentice Hall of India, 2006.
4. G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India, 2006.

5. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill Edition, Tenth Reprint 2007.

M.Tech. Degree Examination :
Branch : Computer Science and Engineering
Specialization : Computer Science and Engineering
Model Question Paper – II : First Semester
MCSCS 105-4 Cloud Computing (Elective I) : (Regular – 2013 Admission onwards)
Time : 3hrs
Maximum : 100 marks
Answer the following Questions.
1a)Explain about the various clod deployment models (7 Marks)
b)Explain the pros and cons of cloud service development (9 Marks)
c) Explain about IBM clouds (9 Marks)
OR

2 a) Explain about the cloud services in detail (9 Marks)
b)DIscuss about on demand computing (6 Marks)
c) Explain infrastructure as a service (IAAS) in cloud (10 Marks)