1.1 Why Data Mining
Data mining transforms large datasets into knowledge.
A data warehouse is a multi-heterogeneous data source that organizes storage in a single site in a unified pattern to support management decisions.
Online analytical Processing (OLAP) is an analytical technique that has the ability to summarize, merge, and aggregate and to view information from different angles. (Note: Different from online transaction processing OLTP)
1.2 What is Data mining
Data mining is the process of mining interesting patterns and knowledge from a large amount of data.
Data mining Process:
- Data cleansing (eliminating noise and deleting inconsistent data)
- Data integration (multiple data sources can be grouped together)
- Data selection (extracts data from the database that are related to the analysis task)
- Data transformations (by aggregating or aggregating operations, transforming and unifying data into the form of appropriate mining)
- Data mining (basic steps, using intelligent methods to extract data patterns)
- Pattern assessment (identifying a truly interesting pattern representing knowledge, based on a measure of interest)
- Knowledge representation (using visualization and knowledge presentation techniques to provide users with knowledge of mining)
1.3 What types of data can be mined
- Database data
- Data Warehouse Data
- Transactional data
- Other types of data (data flow, ordered/sequential data, graph or network data, spatial data, text data, multimedia, and the World Wide Web)
1.4 What types of patterns can be mined
- Characterization and differentiation
- Frequent pattern, association, and correlation mining
- Classification and regression
- Cluster analysis
- Off-Group Point analysis
1.5 What technology to use
- Statistics
- Machine learning
- Database systems and data warehouses
- Information retrieval
1.6 What types of applications are you looking for?
- Business Intelligence
- Web search engine
1.7 Key issues in data mining
- Mining methods
- User interaction
- Availability and Scalability
- Diversity of data types
- Data Mining and social
"Data Mining concepts and technologies" reading notes-Introduction to Chapter I.
