Data Mining and Warehousing

GANPAT UNIVERSITY 

FACULTY OF ENGINEERING & TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (CBA/CS/BDA)

Semester

VI

Version

1.0.0.1

Effective from Academic Year

2022-23

Effective for the batch Admitted in

June 2020

Subject code

2CSE60E27

Subject Name

Data Mining & Warehousing

Teaching scheme 

Examination scheme (Marks)

(Per week)

Lecture (DT)

Practical (Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit 

2

0

1

0

3

Theory 

40

60

100

Hours

2

0

2

0

4

Practical

60

40

100

Pre-requisites:

Basics of Probability & Statistics, Python/JAVA Programming Language, Database Management System

Learning Outcomes:

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 

Theory syllabus

Unit

Content

Hrs

1

Data Warehousing

Basic Concepts, Data Cube and OLAP, Design and Usage, ETL, Implementation

5

2

Data Mining Basics

Importance, Functionalities, Classification, Architecture, Major Issues, Data mining metrics, Applications, Social impacts of data, Data Mining from a Database Perspective

3

3

Data Pre-processing 

Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, Data Reduction, Data Discretization

3

4

Association Rules 

Algorithms, Advanced Association Rule Techniques, Measuring the Quality of Rules

5

5

Classification

Basic issues regarding classification and prediction, Decision Tree, Bayesian classification, K Nearest Neighbour Algorithm, Associative classification, Statistical-Based Algorithms, Rule-Based Classification

7

6

Clustering

Similarity and Distance Measures, Hierarchical Algorithms, Partitioned Algorithms, Clustering Large Databases, Clustering with Categorical Attributes

7

Self-Study Topics

Data Mining Applications, Mining Event Sequences, Visual DM, The WEKA data mining Workbench

Practical Content

Practical contents will be based on following concepts like Web Scraping, Text Mining, Implementation of Classification, Association Rule based and Clustering Algorithms

Mooc Course

Course Name: Data Mining

Link: https://onlinecourses.nptel.ac.in/noc21_cs06/

Text Books

1

J. Han and M. Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufman

Reference Books

1

Alex Berson, Stephen J. Smith, "Data Warehousing, Data Mining, and OLAP", MGH

Course Outcomes:

COs

Description

CO1

Recognize the process of data cleaning and pre-processing for mining applications 

CO2

Apply various frequent pattern mining techniques on various data and understand the importance of Attribute Selection

CO3

Distinguish problems related to classification or clustering

CO4

Evaluate accuracy of various algorithms and select appropriate method for their respective data.

Mapping of CO and PO: