Data Mining

dissimulationpotterData Management

Nov 20, 2013 (3 years and 10 months ago)

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Data Mining


Course Name:
Data Mining

Course Code:
ITF
307

Credit hours:
3

Knowledge Domain:
IT Foundations.

Prerequisite(s):
Database Systems (ITF302)



Learning Objectives

Upon completion of this course, the student will be able to:

1.

Grasp the

concept of d
ata Warehousing& OLAP needed for data
mining together with any preprocessing.

2.

Apply mining association rules.

3.

Acquire

classification& prediction methods and procedures.


Learning Outcomes

1.

Grasping the needs of data mining with respect to the architecture
o
f the data warehouse.

2.

Grasping how to find Association rules in large databases.

3.

Acquaintance with classification and prediction methods.



Overview and Syllabus

Introduction to data mining. Data Warehouse and OLAP technology for
data mining. Data preproc
essing. Data mining primitives, languages and
system architecture. Mining association rules in large databases.
Classification and prediction. Cluster analysis.



Course Outline


Topic

Lecture
Hours

1

Introduction to data mining

Types of databases to be
mined. Data mining functionalities.
Classification of data mining systems.

6

2

Data Warehouse and OLAP technology for data mining

Data warehouses. A multidimensional data model. Data
warehouse architecture and implementation. Online Analytical
Processing
(OLAP) and Online Analytical Mining (OLAM).

6

3

Data Preprocessing

Data cleaning. Data integration and transformation. Data
reduction. Discretization and concept hierarchy generation.

6

4

Data mining primitives, languages and system architecture

Data m
ining primitives. Data mining query languages.
Architecture of data mining systems.

6

5

Mining association rules in large databases

Association rule mining. Mining single
-
and multi
-
dimensional
associating rules from transactional databases and data
wareho
uses.

6

6

Classification and Prediction

Classification by decision tree induction. Bayesian
classification. Prediction. Classification accuracy.

6

7

Cluster analysis

Types of data in cluster analysis. Partitioning methods.
Hierarchical methods. Outlier a
nalysis.

6