GANPAT UNIVERSITY
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FACULTY OF ENGINEERING & TECHNOLOGY
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Programme
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Bachelor of Technology
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Branch/Spec.
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Computer Science & Engineering (CBA/CS/BDA)
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Semester
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VI
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Version
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1.0.0.1
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Effective from Academic Year
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2022-23
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Effective for the batch Admitted in
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June 2020
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Subject code
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2CSE60E27
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Subject Name
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Data Mining & Warehousing
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Teaching scheme
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Examination scheme (Marks)
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(Per week)
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Lecture (DT)
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Practical (Lab.)
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Total
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CE
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SEE
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Total
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L
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TU
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P
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TW
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Credit
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2
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0
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1
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0
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3
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Theory
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40
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60
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100
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Hours
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2
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0
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2
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0
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4
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Practical
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60
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40
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100
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Pre-requisites:
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Basics of Probability & Statistics, Python/JAVA Programming Language, Database Management System
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Learning Outcomes:
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After successful completion of the course, the student will be able to:
- Learn the process of data cleaning and pre-processing for mining applications
- Apply various mining techniques on various types of data
- Distinguish problems related to classification and clustering
- Evaluate accuracy of various classification and clustering algorithms
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Theory syllabus
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Unit
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Content
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Hrs
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1
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Data Warehousing
Basic Concepts, Data Cube and OLAP, Design and Usage, ETL, Implementation
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5
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2
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Data Mining Basics
Importance, Functionalities, Classification, Architecture, Major Issues, Data mining metrics, Applications, Social impacts of data, Data Mining from a Database Perspective
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3
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3
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Data Pre-processing
Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, Data Reduction, Data Discretization
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3
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4
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Association Rules
Algorithms, Advanced Association Rule Techniques, Measuring the Quality of Rules
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5
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5
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Classification
Basic issues regarding classification and prediction, Decision Tree, Bayesian classification, K Nearest Neighbour Algorithm, Associative classification, Statistical-Based Algorithms, Rule-Based Classification
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7
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6
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Clustering
Similarity and Distance Measures, Hierarchical Algorithms, Partitioned Algorithms, Clustering Large Databases, Clustering with Categorical Attributes
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7
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Self-Study Topics
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Data Mining Applications, Mining Event Sequences, Visual DM, The WEKA data mining Workbench
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Practical Content
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Practical contents will be based on following concepts like Web Scraping, Text Mining, Implementation of Classification, Association Rule based and Clustering Algorithms
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Mooc Course
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Course Name: Data Mining
Link: https://onlinecourses.nptel.ac.in/noc21_cs06/
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Text Books
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1
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J. Han and M. Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufman
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Reference Books
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1
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Alex Berson, Stephen J. Smith, "Data Warehousing, Data Mining, and OLAP", MGH
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