B.E. Electronics(Instrumentation Control):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 Skills First year: Semester II Mathematics II Manufacturing Process Chemistry Electrical and Electronic Science Thermodynamics Organizational Behavior Second year: Semester I Electromagnetic Fields Human Values, Ethics and IPR Semiconductor Devices Measurement Science and Techniques Circuit Theory Digital Electronic Circuits Electrical Machines Second year: Semester II Fluid Mechanics Computer System Architecture Optimization Techniques Analog Electronic Circuits Numerical and Statistical Methods Electrical and Electronic Measurements Environmental Studies Third year: Semester I Elements and Analysis of Instrumentation System Analytical Instrumentation Signals and Systems Power Electronics Microprocessors Biomedical instrumentation Summer Training Third year: Semester II Data Acquisition Systems Industrial Measurements Process Dynamics and Control Control Systems Total Quality Management Fourth year: Semester I Advance Process Control Virtual Instrumentation Instrumentation System Design Engineering Economics Microelectronics and ICs 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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