B.E. Electrical Engineering:Data Mining and Pattern Recognition

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skills

First Year: Semester-II

Mathematics II
Manufacturing Process
Chemistry
Electrical and Electronic Science
Thermodynamics
Organizational Behavior


Second Year- Semester - I

Semiconductor Devices
Measurement Science and Techniques
Digital Electronic Circuits
Circuit Theory
Electromechanical Energy conversion
Electromagnetic Fields
Human Values, Ethics and IPR

Second Year- Semester – II

Numerical and Statistical Methods
Fluid Mechanics
Power Generation and Economics
Analog Electronic Circuits
Electrical and Electronic Measurements
Transmission and Distribution of Power
Environmental Studies

Third Year- Semester – I

Power Electronics
Asynchronous Machines
Switch gear and Protection
Microprocessors
Optimization Techniques
Engineering Economics
Summer Training (6 Weeks after 2nd year during summer vacation)


Third Year- Semester – II

Total Quality Management
Control Systems
Synchronous Machines
Power System Analysis
Flexible AC Transmission Systems


Fourth Year- Semester – I

High Voltage Engineering
Operation and Control of Power Systems
Electric Drives
Intelligent Algorithms in Power Systems


Fourth Year- Semester – II

Project Semester
Project
Industrial Training

Data Mining and Pattern Recognition

Data Mining: What is data mining, on what kind of data, Data Mining Functionalities

Data Warehouse: Difference Between operational database systems and data warehouses, A multidimensional data model, Data Warehouse architecture, data warehouse architecture, Data Warehouse implementation

Data preprocessing: Data cleaning, data integration & transformation, data reduction

Data Mining Query Language

Characterization & Comparison, Generalization, Mining association rules in large databases, constraint based association Mining

Classification & prediction Classification by decision Tree Induction, Bayesian classification, classification by Back propagation

Cluster analysis Partitioning Methods, Hierarchical methods, and Density & Grid based methods

Mining complex types of data, applications & trends in data mining, Social impacts of data mining

Pattern recognition: its importance & applications, applications in Bioinformatics, recognizing important bioinformatic sequences, other applications of pattern discovery

Laboratory Work: Implementation of various data mining techniques like classification, clustering, generalization, cleaning etc.