CS2032 DATA WAREHOUSING AND DATA MINING

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DOC/LP/00
/28.02.02




LESSON PLAN


LP


CS2032

LP Rev. No : 01

Date :
0
2
-

0
7
-
20
1
2

Page
:
01 of 06

Sub Code & Sub Name

:
CS2032 DATA WAREHOUSING &
DATA MINING

( Elective )



Branch : CS


Semeste
r :V
II



UN
IT I
DATA WAREHOUSING









10


Data warehousing Components

Building a Data warehouse

-

Mapping the Data

Warehouse
to a Multiprocessor Architecture


DBMS Schemas for Decision Support


Data Extraction,
Cleanup, and Transformation
Tools

Metadata.



Objective :

To introduce the concept

of

Data Warehousing and study in detail about the




various components of the Data warehouse.














Session
No

Topics to be covered

Time

Ref

Teaching
Method

1

Intr
o
duction to Data warehouse

50m

T1

BB

2

Components of Data warehouse

50m

T1

BB

3

Constructing a Data warehouse

-

Business and
Design Considerations

50m

T1

BB

4

Constructing a Data warehouse


Technical and
Implementation Considerations

50m

T1

BB

5

Mapping to a Multiprocessor Architect
ure

50m

T1

BB

6

DBMS Schemas


Star Schema

50m

T1

BB

7

DBMS Schemas
-
Star join and Starindex

50m

T1

BB

8

Tools for Data Extraction and Clean up

50m

T1

BB

9

Tolls for Transformation

50m

T1

BB

10

Metadata repository and management

50m

T1

BB






DOC/LP/00
/28.02.02




LESSON PLAN


LP


CS2032

LP Rev. No : 0
1

Date :
0
2
-
0
7
-
201
2

Page :02

of 06

Sub Code & Sub Name

:
CS2032 DATA WAREHOUSING &
DATA MINING

( Elective )



Branch : CS



Semester :V
II





UNIT II

BUSINESS ANALYSIS







8


Reporting and Query tools and Applications


Tool Categories


The Need for

Applications


Cognos Impromptu


Online Analytical Processing (OLAP)


Need

Multidimensional Data
Model


OLAP Gu
idelines


Multidimensional versus

Multirelational OLAP


Categories of
Tools


OLAP Tools and the Internet.



Objective :


To stu
dy

about the
need for
OLAP tools and categorization of

OLAP t
ools

in business.












Session
No

Topics to be covered

Time

Ref

Teac
hing
Method

1
1

Reporting and Query Tools

50m

T
1

BB

1
2

T
ool Categories

50m

T
1

BB

1
3

Need for Applications

50m

T1

BB

1
4

Cognos Impromptu

50m

T1

BB

1
5

Multidimensi
onal

data model

50m

T1

BB

1
6

Need for OLAP

50m

T1

BB

1
7

Categorization of OLAP Tools

50m

T1

BB

1
8

OLAP Tools and the Internet

50m

T1

BB

19

CAT
-
1

60m





DOC/LP/00
/28.02.02




LESSON PLAN


LP


CS2032

LP Rev. No : 0
1

Date :
0
2
-

0
7
-
201
2

Page :03

of 06

Sub Code & Sub Name

:
CS2032 DATA WAREHOUSING &
DATA MINING

( Elective

)



Branch : CS


Semester :V
II





UNIT III


DATA MINING








8


Introduction


Data


Types of Data


Data Mining Functionalities


Interestingness of

p
atterns


Classification of Data Mining Syste
ms


Data Mining Task Primitives

Integration
of a Data Mining System with a Data Warehouse


Issues

Data

Preprocessing.



Objective :
To study

about the concepts
and classification
of
Data mining systems.

















Session
No

Topics to be covered

Time

Ref

Teach
ing
Method

20

Introduction to d
ata Mining

50m

T
2

BB

2
1

Functionalities of data mining

50m

T2

BB

22

Classification of data mining systems

50m

T2

BB

23

Data mining task primitives

50m

T2

BB

24

Integration of data mining with data warehousing

50m

T2

BB

25

Issues in data mining

50m

T2

BB

26

Data Preprocessing

50m

T2

BB

27

Data Preprocessing


Co湴搮

㔰5








DOC/LP/00
/28.02.02




LESSON PLAN


LP


CS2032

LP Rev. No : 0
1

Date :
0
2
-

07
-
2012

Page :04

of 06

Sub Code & Sub Name

:

CS2032 DATA WAREHOUSING &
DATA MINING

( Elective )



Branch : CS


Semester :V
II








UNIT IV

ASSOCIATION RULE MINING AND CLASSIFICATION



11


Mining Frequent Patterns, Associations and Correlations



Mining Methods


Mining

Various Kinds of Association Rules


Correlation Analysis


Constraint Based Association
Mining


Classification and Prediction
-

Basic Concepts
-

Decision Tree Induction
-

Bayesian
Classification


Rule Based Classification


C
lassification by Back

propagation


Support
Vector Machines


Associative Classification


Lazy Learners


Other Classification
methods
-

Prediction



Objective :

To study about the association rule mining and various types of

classification methods.
.




Session
No

Topics to be covered

Time

Ref

Teaching
Method

28

Frequent pattern mining

50m

T2

BB

29

Associations and Correlation methods

50m

T2

BB

3
0

Mining various Association rules

50m

T2

BB

31

Constraint based Association mining

50m

T2

BB

32

Classific
ation and Prediction

50m

T2

BB


33

Decision Tree induction

50m

T2

BB

34

Rule based classification

50m

T2

BB

35

Classification by back propagation and support vector
machines

50m

T2

LCD

36

CAT
-
II

60m



37

Associative Classification

50m

T2

BB

38

L
azy learners

50m

T2

BB

39.

Prediction methods

50m

T2

LCD





DOC/LP/00
/28.02.02




LESSON PLAN


LP


CS2032

LP Rev. No : 0
1

Date :
0
2
-

0
7
-
2012

Page :05
of 06

Sub Code & Sub Name

:
CS2032 DATA WAREHOUSING &
DATA MINING

( Elective )



Branch : C
S


Semester :V
II




UNIT V
CLUSTERING AND APPLICATIONS AND TRENDS IN DATA MINING

8


Cluster Analysis
-

Types of Data


Categorization of Major Clustering Methods


K

means



Partitioning Methods


Hi
erarchical Methods
-

Density
-
Based Methods

Grid Based

Methods


Model
-
Based Clustering Methods


Clustering High Dimensional Data
-

Constraint


Based Cluster Analysis


Outlier Analysis


Data Mining Applications.




Objective :


To study
about the c
lustering methods and the applications of
data mining

in various fields.









Session
No

Topics to be covered

Time

Ref

Teaching
Method

40

Cluster analysis and major clustering methods.

50m

T2

LCD

41

K
-
means and Partitioning methods

50m

T2

LCD

42

Hierarchi
cal and Density based methods.

50m

T2

LCD

43

Grid and Model based Clustering methods

50m

T2

LCD

44

Outlier analysis

50m

T2

BB

45

Applications of oulier analysis

50m

T2

LCD

46

Data Mining applications in financial data analysis

50m

T2

LCD

47

Data Minin
g applications in retail industry

50m

T2

LCD

48

Data Mining applications in telecommunication

50m

T2

LCD

49

Data Mining applications biological sciences and
intrusion detection

50m

T2

LCD


50

CAT


III

60m





DOC/LP/00
/28.02.02




LESSO
N PLAN


LP


CS2032

LP Rev. No : 0
1

Date : 29
-

06
-
2011

Page :06

of 06

Sub Code & Sub Name

:
CS2032 DATA WAREHOUSING &
DATA MINING

( Elective )



Branch : CS


Semester :V
II



Course Delivery Plan :



Text Books

1. Alex Berson and Stephen J. Smith, “ Data Warehousing, Data Min
ing & OLAP”, Tata

McGraw


Hill Edition, Tenth Reprint 2007.

2. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Second

Edition, Elsevier, 2007.



References
:


1. Pang
-
Ning Tan, Michael Steinbach and Vipin Kumar, “ Introduction To Da
ta Mining”,

Person Education, 2007.

2. K.P. Soman, Shyam Diwakar and V. Ajay “, Insight into Data mining Theory and

Practice”, Easter Economy Edition, Prentice Hall of India, 2006.

3. G. K. Gupta, “ Introduction to Data Mining with Case Studies”, Easter Ec
onomy

Edition, Prentice Hall of India, 2006.

4. Daniel T.Larose, “Data Mining Methods and Models”, Wile
-
Interscience, 2006




Prepared by

Approved by

Signature




Name

M
s.
V
.
V
id
h
ya.

Ms.Kalavathi


Dr.
Tivakaran

Designation

Assistant Professor
, CSE

Assist
ant Professor
, CSE


Professor &
HOD,


Department of CS
E

Date

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7
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2

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2



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