Page 78 - B.Tech IT Curriculum and Syllabus R2017 - REC

Department of IT, REC

3. Validate and critically assess a mathematical proof.
4. Use a combination of theoretical knowledge and independent mathematical thinking in
creative investigation of questions in graph theory.
5. Reason from definitions to construct mathematical proofs.

REFERENCES:
1. Narsingh Deo, ―Graph Theory: With Application to Engineering and Computer
Science‖, Prentice Hall of India, 2003.
2. Grimaldi R.P. ―Discrete and Combinatorial Mathematics: An Applied Introduction‖,
Addison Wesley, 1994.
3. Clark J. and Holton D.A, ―A First Look at Graph Theory‖, Allied Publishers, 1995.
4. Mott J.L., Kandel A. and Baker T.P. ―Discrete Mathematics for Computer Scientists
and Mathematicians‖, Prentice Hall of India, 1996.
5. Liu C.L., ―Elements of Discrete Mathematics‖, Mc Graw Hill, 1985.
6. Rosen K.H., ―Discrete Mathematics and Its Applications‖, Mc Graw Hill, 2007.

IT17E62 DATA WAREHOUSING AND DATA MINING L T P C
3 0 0 3

OBJECTIVES: The student should be made to:
 Learn the concepts of Data Warehousing and Business Analysis.
 Familiar with the concepts of Data Mining.
 Understand the concepts of Association and Correlations Algorithms.
 Understand the concepts of Classification Algorithms.
 Understand the concepts of Clustering and outlier Analysis.

UNIT I DATA WAREHOUSING 9
Data Warehouse: Basic Concepts, A Multitier Architecture, Data Warehouse Models, Metadata
Repository- Data Warehouse Modelling: Data Cube and OLAP, Data Cube: A Multidimensional Data
Model: Schemas-Concept Hierarchies-OLAP Operations.
(TB1-CH: 4)

UNIT II DATA MINING AND VISUALIZATION 8
Introduction: Kinds of Data, Kinds of Patterns- Data Objects and Attribute Types- Data Visualization:
Pixel-Oriented, Geometric Projection, Icon-Based, Hierarchical, Visualizing Complex Data and
Relations- Data Preprocessing.
(TB1-CH: 1&2)

UNIT III ASSOCIATIONS AND CORRELATIONS 9
Basic Concepts: Frequent Itemsets , Closed Itemsets, and Association Rules - Frequent Item set
Mining Methods: Finding Frequent Itemsets by Confined Candidate Generation , Growth Approach
for Mining Frequent Itemsets , Mining Frequent Itemsets Using Vertical Data Format , Mining Closed
and Max Patterns - Interesting Patterns: Pattern Evaluation Methods.
(TB1-CH: 6)

UNIT IV CLASSIFICATION 9
Basic Concepts- Decision Tree Induction: ID3- Bayes Classification Methods: Bayes‘ Theorem,
Naive Bayesian Classification- Classification by Back propagation- Support Vector Machines-
Techniques to improve classification accuracy-Prediction.
(TB1-CH: 8)

Curriculum and Syllabus | B.Tech. Information Technology | R2017 Page 78