Download Data Mining

Data Mining
Course Name: Data Mining
Course Code: ITF307
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 data 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
of 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 preprocessing. Data mining primitives, languages and
system architecture. Mining association rules in large databases.
Classification and prediction. Cluster analysis.
Course Outline
Topic
1 Introduction to data mining
Types of databases to be mined. Data mining functionalities.
Classification of data mining systems.
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).
3 Data Preprocessing
Data cleaning. Data integration and transformation. Data
reduction. Discretization and concept hierarchy generation.
4 Data mining primitives, languages and system architecture
Data mining primitives. Data mining query languages.
Architecture of data mining systems.
Lecture
Hours
6
6
6
6
5 Mining association rules in large databases
Association rule mining. Mining single-and multi-dimensional
associating rules from transactional databases and data
warehouses.
6 Classification and Prediction
Classification by decision tree induction. Bayesian
classification. Prediction. Classification accuracy.
7 Cluster analysis
Types of data in cluster analysis. Partitioning methods.
Hierarchical methods. Outlier analysis.
6
6
6