Download Lecture-Plan-of-MCA-1031-5th

Lecture Plan for MCA Course
Semester: V, Paper: Data Warehousing and Data Mining (MCA 1031)
Sl.
No.
1
Topics to be covered
Data warehousing& Data Mining: Introduction, What is a Data
Warehousing Definition
2
Multidimensional Data Model
3
OLAP Operation, warehouse Scheme
4
Data Warehousing, Architecture
5
Metadata, OLAP ENGINE
6
Data warehouse Backend Process
7
Introduction to Data Mining
8
What is Data Mining, Data Mining Definition
9
KDD Vs Data Mining, DBMS Vs. DM
10
Other related area, DM Technique
11
Other Mining Problem, Issue and challenge is in DM
12
DM Application area, DM Application
13
Mining Association Rule in Large Database: Introduction
14
What is an Association Rule, Method to discover association Rule
15
A Priori Algorithm, Partition Algorithm
16
Pincer- Search algorithm, Dynamic item set Counting Algorithm
17
FP – Tree Growth Algorithm, Discussion and Different Algorithm
18
Generalized, Association Rule
19
Association Rules with Item Constraints
20
Advance Data Mining Techniques and tools
21
Clustering Techniques: Introduction
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22
Clustering Paradigm
23
Partition Algorithm, K-Medoid Algorithm
24
CLARA, CLARANS, Hierarchical Clustering, DBSCAN, BIRCH,
CURE
25
Categorical Clustering Algorithms, STIRR, ROCK, CACTUS
26
Data Mining Primitives, Language and System Architecture : Data
Mining Primitives
27
What defines a Data Mining task, Task relevant Data
28
The Kind of Knowledge to Mined, Concept Hierarchy
29
Interestingness Measure, presentation and visualization of Discovered
Patterns
30
Data Mining Query Language
31
Decision Trees: Introductions, What is decision Tree, Tree Construction
Principle
32
Best split splitting Indices, Splitting criteria
33
Decision Tree Construction with Presenting
34
Pruning Technique
35
Integration of Pruning Technique and Construction
36
Temporal and Spatial Data Mining: Introduction
37
What is Temporal Data Mining Temporal Association Rules
38
Sequence Mining, The GSP Algorithm
39
SPIRIT, Spatial Mining
40
Spatial Clustering, Spatial Trends
Text Book:
1. A.K. Pujari, “A Data Mining Technique”, University press (India) Limited,
2001
Reference Books:
1. A Hand and M. Kamber, “Data Mining Concept and Technique”, Morgan.
Kauffmann Publishers, Else river India, New Delhi, 2003.
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2.
3.
4.
5.
Recherd J, Roiger and Michance W. Creatz, Data Mining: a tutorial Based
Primer, Addision Wesley, 2003.
M.H. Dienham, Data Mining : Introductory and Advanced Topics, Pentice Hall
2003.
Database Management Systems (DBMS) by Icon Group International .
Database Systems Concepts "KORTH”.
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