1 Introduction:
- What motivated data mining? What is data Mining?
- Types of database (Relational database, data warehouses, transactional database)
- Functionalities of data mining – what kinds of pattern can be mined?
- Association Analysis, cluster Analysis, outlier analysis, evolution analysis
- Stages of knowledge discovery in database (KDD)
- Setting up a KDD Environment
- Issues in data warehouse and Data Mining
- Application of Data Warehouse and Data mining
2 Data Warehousing for Data Mining:
- Differences between operational database systems and
- data warehouses
- Data Warehouse Architecture
- Distributed and Virtual Data Warehouse
- Data Warehouse Manager
- Data marts, Metadata, Multidimensional data model
- From Tables and Spread Sheets to Data Cubes
- Star schema, Snowflake schema and Fact constellation
- Schema
3 OLAP Technology for Data Mining:
- On-line analytical processing models and operations
- (drill down, drill up, slice, dice, pivot)
- Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP
- OLTP
4 Tuning for Data Warehouse:
- Computation of Data Cubes, modeling
- OLAP data, OLAP queries
- Data Warehouse back end tools
- Tuning and testing of Data Warehouse
5 Data Mining Techniques:
- Data Mining definition and Task
- KDD versus Data Mining
- Data Mining techniques, tools and application
6 Data Mining Query Languages:
- Data mining query languages
- Data specification, specifying knowledge, hierarchy
- specification, pattern presentation & visualization
- specification
- Data mining languages and standardization of data
- Mining
7 Association Analysis:
- Association Rule Mining (Market basket analysis)
- Why Association Mining is necessary?
- Pros and Cons of Association Rules
- Apriori Algorithm
8 Cluster Analysis, Classification and Predication:
- What is classification? What is predication?
- Issues regarding classification and prediction (Preparing
- the data for classification and prediction, Comparing
- classification methods)
- Classification by decision tree induction (Extracting
- classification rules from decision trees)
- Bayesian Classification
- Classification by back propagation
- Introduction to Regression (Types of Regression)
- Clustering Algorithm (K-mean and K-Mediod Algorithms)
9 Advanced Concepts in Data Mining:
- Mining Text Databases.
- Mining the World Wide Web
- Mining Multimedia and Spatial Databases
