B.E. Computer Science & Engineering:Data Mining and Pattern Recognition
Bachelor
In Patiala
Description
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Type
Bachelor
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Location
Patiala
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Duration
4 Years
Facilities
Location
Start date
Start date
Reviews
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.
B.E. Computer Science & Engineering:Data Mining and Pattern Recognition