B.E. Electronics & Comm. Engg:Data Mining and Pattern Recognition

Thapar University
In Patiala

Price on request

Important information

  • Bachelor
  • Patiala

Important information

Where and when

Starts Location
On request
Thapar University P.O Box 32, 147004, Punjab, India
See map

Course programme

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Solid Mechanics
Communication Skills

First year: Semester II

Mathematics II
Manufacturing Process
Electrical and Electronic Science
Organizational Behavior

Second year: Semester I

Numerical and Statistical Methods
Measurement Science and Techniques
Electromagnetic Fields
Semiconductor Devices
Signals and Systems
Digital Electronic Circuits
Human Values, Ethics and IPR

Second year: Semester II

Optimization Techniques
Analog Electronic Circuits
Networks and Transmission Lines
Electrical Engineering Materials
Analog Communication Systems
Data Structure and Information Technology
Environmental Studies

Third year: Semester I

Digital Signal Processing for Communications Microprocessors
VLSI Circuit Design
Digital Communication Systems
Microelectronics Technology
Linear Integrated Circuits and Applications
Summer Training(6 weeks)

Third year: Semester II

Project Semester
Industrial Training(6 weeks)

Fourth year: Semester I

Antenna and Wave Propagation
Modern Control Engineering
Wireless and Mobile Communication Systems
Microwave Engineering
Engineering Economics

Fourth year: Semester II

Optical Communication Systems
Advanced Communication Systems
HDL Based Digital Design
Total Quality Management
Minor Project

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.