B.E. Computer Science & Engineering:Data Mining and Pattern Recognition

Thapar University
In Patiala

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Important information

  • Bachelor
  • Patiala
  • Duration:
    4 Years
Description

Important information
Venues

Where and when

Starts Location
On request
Patiala
Thapar University P.O Box 32, 147004, Punjab, India
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Course programme


First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skill

First Year: Semester-II

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

Second Year- Semester - I

Measurement Science and Techniques
Optimization Techniques
Semiconductor Devices
Data Structures
Discrete Mathematical Structures
Digital Electronic Circuits
Human Values, Ethics and IPR

Second Year- Semester – II

Numerical and Statistical Methods
Electrical Engineering Materials
Computer System Architecture
Principles of Programming Languages
Analysis and Design of Information Systems
Operating Systems
Environmental Studies


Third Year- Semester – I

Object Oriented Programming
Theory of Computation
Computer Networks
Data Base Management Systems
Software Engineering
Microprocessors
Summer Training

Third Year- Semester – II

Total Quality Management
Algorithm Analysis and Design
Software Project Management
Internet and Web Technologies

Fourth Year- Semester – I

Engineering Economics
System Software
Compiler Construction
Computer Graphics
Artificial Intelligence

Fourth Year- Semester – II

Project Semester
Project
Industrial Training(6 weeks)


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.


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