CT75 DATA WAREHOUSING
& DATA MINING
STRUCTURE No
of hours (42)
1. Overview and Concepts 1
hours
2. The Building Blocks 2
hours
3. Multidimensional Data Model 3
hours
4. Extraction, Transformation and Loading 2 hours
5. OLAP 2
hours
6. Data Warehouse Implementation 2 hours
7. From Data Warehousing to Data Mining 1 hours
8. Introduction to Data Mining 1
hours
9. Data Preprocessing 4
hours
10. Classification and Prediction 8
hours
11. Mining Frequent Patterns and association
Rules 6 hours
12. Clustering and Cluster Analysis 6 hours
13. Applications and Trends in Data Mining 4 hours
Details:
1. Overview and Concepts
1.1
Failures
of past decision-support systems
1.2
Operational
vs.Decision Support systems
1.3
Definition
of Data Warehouse
2. The Building Blocks
2.1
Defining
Features
2.2
Data
Mart
2.3
Components
2.4
Metadata
3. Multidimensional Data Model
3.1
From
Tables and Spreadsheets to Data Cubes
3.2
Star,Snowflakes,and
fact constellation:Schemas for Multidimensional Databases
3.3
Examples
3.4
Measures:
Categorizations and Computation
3.5
Concept
Hierarchy’
4. Extraction, Transformation and Loading
4.1
Data
Extraction
4.2
Data
Transformation
4.3
Data
Loading
5. OLAP
5.1
OLAP
Operations in Multidimensional Data Model
5.2
A
Starnet Query Model for Querying Multidimensional Databases
5.3
Types
of OLAP Servers
6. Data Warehouse Implementation
6.1
Efficient
Computation of Data Cubes
6.2
Indexing
OLAP Data
6.3
Efficient
Processing of OLAP Data
7. From Data Warehousing to Data Mining
7.1
Data
Warehouse Usage
7.2
On-Line
Analytical Processing, on-line Analytical Mining
8. Introduction to Data Mining
8.1
Data
Mining and Its Functionalities
8.2
Issues
in Data Mining
8.3
Input:Concept,
Instances, Attributes
9. Data Preprocessing
9.1
Descriptive
Data Summarization
9.2
Data
Cleaning
9.3
Data
Integration and Transformation
9.4
Data
Reduction
9.5
Data
Discretization and Concept Hierarchy Generation
10. Classification
10.1
Basic
Concepts, Output Knowledge Representation
10.2
Decision
Tree-based Classification
10.3
Classification
Rule-based Classification
10.4
Bayesian
Classification
10.5
Neural
Network-based classification
10.6
Models
for Numerica prediction
10.7
Performance
Evaluation of Classifiers and Prediction Models
11. Mining Frequent Patterns and Association
Rules
11.1
Basic
Concepts
11.2
Frequent
Pattern Mining Methods
11.3
Mining
Various kinds of Association Rules
12. Clustering and Cluster Analysis
12.1
Types
of Data in Cluster Analysis
12.2
Major
Clustering methods
12.3
Partitioning
Methods
12.4
Hierarchical
Methods
12.5
Density-based
Methods
12.6
Model
based Methods
12.7
Outlier
Analysis
13.
Applicatio9ns
and Trends in Data Mining: Mining
Stream, Time Series, Sequence Data, Graphics etc.
Text Books:
Reference Books