COMPUTER SCIENCE AND ENGINEERING
YEAR OF
CATEGORY L T P CREDIT INTRODUCTION
CST466 DATA MINING
PEC 2 1 0 3 2019
Preamble: This course helps the learner to understand the concepts of data mining and data
warehousing. It covers the key processes of data mining, data preprocessing techniques,
fundamentals and advanced concepts of classification, clustering, association rule mining, web
mining and text mining. It enables the learners to develop new data mining algorithms and apply
the existing algorithms in real-world scenarios.
Prerequisite: NIL
Course Outcomes: After the completion of the course the student will be able to
CO# CO
CO1 Employ the key process of data mining and data warehousing concepts in application
domains. (Cognitive Knowledge Level: Understand)
CO2 Make use of appropriate preprocessing techniques to convert raw data into suitable
format for practical data mining tasks (Cognitive Knowledge Level: Apply)
CO3 Illustrate the use of classification and clustering algorithms in various application
domains (Cognitive Knowledge Level: Apply)
CO4 Comprehend the use of association rule mining techniques. (Cognitive Knowledge
Level: Apply)
CO5 Explain advanced data mining concepts and their applications in emerging domains
(Cognitive Knowledge Level: Understand)
Mapping of course outcomes with program outcomes
PO PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO1 PO11 PO1
1 0 2
CO1
CO2
CO3
Downloaded from Ktunotes.in
, COMPUTER SCIENCE AND ENGINEERING
CO4
CO5
Abstract POs defined by National Board of Accreditation
PO# Broad PO PO# Broad PO
PO1 Engineering Knowledge PO7 Environment and Sustainability
PO2 Problem Analysis PO8 Ethics
PO3 Design/Development of solutions PO9 Individual and team work
Conduct investigations of
PO4 complex problems PO10 Communication
PO5 Modern tool usage PO11 Project Management and Finance
PO6 The Engineer and Society PO12 Lifelong learning
Assessment Pattern
Continuous Assessment Tests
Bloom’s End Semester Examination
Category Marks (%)
Test 1 (%) Test 2 (%)
Remember 20 20 20
Understand 30 30 30
Apply 50 50 50
Analyze
Evaluate
Create
Mark Distribution
Total Marks CIE Marks ESE Marks ESE Duration
150 50 100 3
Downloaded from Ktunotes.in
YEAR OF
CATEGORY L T P CREDIT INTRODUCTION
CST466 DATA MINING
PEC 2 1 0 3 2019
Preamble: This course helps the learner to understand the concepts of data mining and data
warehousing. It covers the key processes of data mining, data preprocessing techniques,
fundamentals and advanced concepts of classification, clustering, association rule mining, web
mining and text mining. It enables the learners to develop new data mining algorithms and apply
the existing algorithms in real-world scenarios.
Prerequisite: NIL
Course Outcomes: After the completion of the course the student will be able to
CO# CO
CO1 Employ the key process of data mining and data warehousing concepts in application
domains. (Cognitive Knowledge Level: Understand)
CO2 Make use of appropriate preprocessing techniques to convert raw data into suitable
format for practical data mining tasks (Cognitive Knowledge Level: Apply)
CO3 Illustrate the use of classification and clustering algorithms in various application
domains (Cognitive Knowledge Level: Apply)
CO4 Comprehend the use of association rule mining techniques. (Cognitive Knowledge
Level: Apply)
CO5 Explain advanced data mining concepts and their applications in emerging domains
(Cognitive Knowledge Level: Understand)
Mapping of course outcomes with program outcomes
PO PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO1 PO11 PO1
1 0 2
CO1
CO2
CO3
Downloaded from Ktunotes.in
, COMPUTER SCIENCE AND ENGINEERING
CO4
CO5
Abstract POs defined by National Board of Accreditation
PO# Broad PO PO# Broad PO
PO1 Engineering Knowledge PO7 Environment and Sustainability
PO2 Problem Analysis PO8 Ethics
PO3 Design/Development of solutions PO9 Individual and team work
Conduct investigations of
PO4 complex problems PO10 Communication
PO5 Modern tool usage PO11 Project Management and Finance
PO6 The Engineer and Society PO12 Lifelong learning
Assessment Pattern
Continuous Assessment Tests
Bloom’s End Semester Examination
Category Marks (%)
Test 1 (%) Test 2 (%)
Remember 20 20 20
Understand 30 30 30
Apply 50 50 50
Analyze
Evaluate
Create
Mark Distribution
Total Marks CIE Marks ESE Marks ESE Duration
150 50 100 3
Downloaded from Ktunotes.in
