Download SRM UNIVERSITY FACULTY OF ENGINEERING AND

SRM UNIVERSITY
FACULTY OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
COURSE PLAN
Course Code: CS 0424
Course Title: Data Mining
Semester: VIII
Course Time: Jan -May 2013
Day
A
Hour
B
Timing
Hour
C
Timing
Hour
D
Timing
Hour
E
Timing
Hour
Timing
Day I
Day II
3rd
10.35-11.25
3rd
10.35-11.25
3rd
10.35-11.25
3rd
10.35-11.25
3rd
10.35-11.25
1,3
8.45-9.35,
1,3
8.45-9.35,
1,3
8.45-9.35,
1,3
8.45-9.35,
1,3
8.45-9.35,
Day III
2
2
9.35-10.25
10.35-11.25
9.35-10.25
10.35-11.25
10.35-11.25
2
9.35-10.25
10.35-11.25
2
9.35-10.25
10.35-11.25
2
9.35-10.25
Location: S.R.M.E.C-Tech Park
Faculty Details:
Sec
A
Name
Prof.C.MALATHY
B
Prof .C.Malathy
Mr.N.Praveen
Mr.N.praveen
Mr.V.Deepan
C
D
Ms.S.Nagadevi
E
Mr.V.Deepan
Chakravarthy
Office
Tech
Park
Tech
Park
Tech
Park
Office
Monday to
Friday
Monday to
Friday
Monday to
Friday
Tech
Park
Tech
Park
Monday to
Friday
Monday to
Friday
Mail id
[email protected]
[email protected]
[email protected]
[email protected]
"nagadevi s" <[email protected]>
[email protected]
TEXT BOOKS
1. Margaret H. Dunham, S. Sridhar "Data Mining Introductory & Advance Topics" - 2006(Unit - 1,2,3,4)
2. C.S.R. Prabhu, “Data Warehousing: Concept, Techniques, Products and Applications”, Prentice Hall of
India, 2001 (Unit-5).
REFERENCE BOOKS
1. J.Han, M.Kamber, “Data Mining: Concept and Techniques”, Academic Press,
Morgan Kanfman Publishers, 2001.
2. Pieter Adrians, Dolf Zantinge, “Data Mining”, Addison Wesley, 2000.
Prerequisite:
NIL
Objectives
•
•
•
Data mining techniques and algorithms
Data Mining environments and applications
Spatial Mining, temporal Mining Algorithms
Assessment Details Attendance : 5 marks Cycle Test – I Surprise Test – I : : 10 marks 5 marks Cycle Test – II : 10 marks Model Exam : 20 marks Test Schedule S.No. 1 2 3 DATE TEST Cycle Test ‐ I Cycle Test ‐ II Model Exam TOPICS Unit I & II Unit III & IV All 5 units DURATION 2 periods 2 periods 3 Hrs Course outcome To Understand Data mining techniques and algorithms To understand Data Mining environments and applications To Understand problems involved
with statistical processing of large
databases.
Ability to identify associations, classes, and clusters
in large data-sets. Program outcome To apply knowledge of computing and mathematics
appropriate to the discipline.
To use current techniques, skills, and tools
necessary for real time systems Detailed Session Plan
Introduction:
Data Mining Tasks, Data mining Issues, Decision Support System, Dimensional Modeling, Data warehousing,
OLAP & its tools, OLTP.
Sessi
Time
Teaching
Topics to be covered
Ref
Testing Method
on
(min)
Method
No.
1. Data Mining Tasks
T1
PPT/BB
Discussion
50
2.
Data Mining Tasks
3.
Data mining Issues
4.
Decision Support System
5.
Dimensional Modeling
6
7.
8.
Data warehousing
9.
OLTP
T1
PPT/BB
Discussion
50
T1
PPT/BB
Test
50
T1
PPT/BB
Discussion
T1
PPT/BB
Viva
T1
PPT/BB
Discussion
T1
PPT/BB
Discussion
T1
PPT/BB
Test
50
50
50
OLAP & its tools
50
50
Mining Techniques: Classification
Introduction, statistical Perspective of data mining, Decision tree, Neural networks, Genetic algorithms, Issues
in classification, Statistitical based algorithm(regression), Distance based algorithm(simple approach),
Decision Tree based algorithm(C4.5), Neural network based (propogation).
10
11.
12.
Introduction, statistical Perspective of data
mining
Decision tree
13.
Neural networks
14.
Genetic algorithms
50
50
50
T1
PPT/BB
Discussion
T1
PPT/BB
T1
PPT/BB
Objective type test
Quiz
Quiz
T1
PPT/BB
Objective type test
50
15.
Issues in classification
50
T1
PPT/BB
Viva
16.
Statistitical based algorithm(regression)
50
T1
PPT/BB
Discussion
17.
Distance based algorithm(simple
approach)
50
T1
PPT/BB
Discussion
18.
Decision Tree based algorithm(C4.5) ,
Neural network based (propogation).
50
T1
PPT/BB
Test
Mining Techniques : Clustering and Association Rules
Introduction to clustering, Similarity and distance measures, Hierarchical algorithm(divisive clustering),
partitional algorithm (Minimum Spanning tree, nearest neighbour), Clustering large database(CURE),
Introduction to association, basic algorithm(Apriori), parallel & distributed(data parallelism), Incremental
l 19 Introduction
i i
l to clustering,
h i
( Similarity
li dand l i l l l)
Discussion
50
T1 PPT/BB
20 distance measures
21 Hierarchical algorithm(divisive clustering)
Discussion
T1 PPT/BB
50
22
23
partitional algorithm (Minimum Spanning
tree, nearest neighbour)
Clustering large database(CURE)
24
25
26
Introduction to association, basic
algorithm(Apriori)
parallel & distributed(data parallelism),
50
50
50
50
T1
PPT/BB
Test
T1
PPT/BB
Objective type test
T1
PPT/BB
Discussion
T1
PPT/BB
Discussion
27
Incremental rules, Association rule
Test
T1 PPT/BB
50
techniques(Generalized, multiple level)
ADVANCED MINING
Web mining, Web content mining, Introduction to Spatial mining & its primitives, spatial classification
algorithm (ID3 extension), Spatial clustering algorithm (SD), Introduction to temporal mining, Time series,
Temporal association rule.
28
Web mining, Web content mining
29
31
Introduction to Spatial mining & its
primitives
spatial classification algorithm (ID3
extension),
Spatial clustering algorithm(SD),
32
Introduction to temporal mining
30
T1
PPT/BB
T1
PPT/BB
Group discussion
Quiz
Objective type test
Quiz
Quiz
T1
PPT/BB
Quiz
T1
PPT/BB
Quiz
T1
50
50
50
50
50
PPT/BB
Quiz
33 Time series
T1
PPT/BB
50
Objective type test
34
35 Temporal association rule.
Quiz
T1
PPT/BB
50
36
Objective type test
DATA MINING ENVIRONMENT
Case study in building business environment, Application of data mining in Government National data
warehouse and case studies.
37 Case Studies in Building Business
Quiz
50
T2
BB
38 Environment
39.
40
41
42
Application of Data Mining – Tamilnadu
Government Data Warehouse
50
T2
BB
Quiz
Objective type test
43
44
45
Data Mining in National Data Warehouse
Ministry of Commerce, World Bank
50
T2
BB
Group discussion
Quiz
•
•
BB – Black Board
PP – Power Point
Prepared by
Approved By
C.MALATHY
HOD/CSE