1. Data Mining: Concepts and Techniques — Slides for Textbook — — Appendix B —
©Jiawei Han and Micheline Kamber
Intelligent Database Systems Research Lab
School of Computing Science
Simon Fraser University, Canada
November 9, 2018
Data Mining: Concepts and Techniques

2. Appendix B. An Introduction to DBMiner
System Architecture
Input and Output
Data Mining Tasks Supported by the System
Support for Task and Method Selection
Support for KDD Process
Main Applications
Current Status
November 9, 2018
Data Mining: Concepts and Techniques

3. System Architecture
DBMiner: A data mining system originated in Intelligent Database Systems Lab and further developed by DBMiner Technology Inc.
OLAM (on-line analytical mining) architecture for interactive mining of multi-level knowledge in both RDBMS and data warehouses
Mining knowledge on Microsoft SQLServer 7.0 databases and/or data warehouses
Multiple mining functions: discovery-driven OLAP, association, classification and clustering

4. Data Mining: Concepts and Techniques
Input and Output
Input: SQLServer 7.0 data cubes which are constructed from single or multiple relational tables, data warehouses or spread sheets (with OLEDB and RDBMS connections)
Multiple outputs
Summarization and discovery-driven OLAP: crosstabs and graphical outputs using MS/Excel2000
Association: rule tables, rule planes and ball graphs
Classification: decision trees and decision tables
Clustering: maps and summarization graphs
Others:
Data and cube views
Visualization of concept hierarchies
Visualization for task management
Visualization of 2-D and 3-D boxplots
November 9, 2018
Data Mining: Concepts and Techniques

5. Data Mining: Concepts and Techniques
Data Mining Tasks
DBMiner covers the following functions
Discovery-driven, OLAP-based multi-dimensional analysis
Association and frequent pattern analysis
Classification (decision tree analysis)
Cluster analysis
3-D cube viewer and analyzer
Other function
OLAP service, cube exploration, statistical analysis
Sequential pattern analysis (under development)
Visual classification (under development)
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Data Mining: Concepts and Techniques

6. Data Mining: Concepts and Techniques
DBMiner Data and Mining Views (Working Panel)
DBMiner Manager
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Data Mining: Concepts and Techniques

7. Data Mining: Concepts and Techniques
OLAP (Summarization) Display Using MS/Excel 2000
November 9, 2018
Data Mining: Concepts and Techniques

8. Data Mining: Concepts and Techniques
Market-Basket-Analysis (Association)—Ball graph
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Data Mining: Concepts and Techniques

9. Data Mining: Concepts and Techniques
Display of Association Rules in Rule Plane Form
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Data Mining: Concepts and Techniques

10. Data Mining: Concepts and Techniques
Display of Decision Tree (Classification Results)
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Data Mining: Concepts and Techniques

11. Data Mining: Concepts and Techniques
Display of Clustering (Segmentation) Results
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Data Mining: Concepts and Techniques

12. Data Mining: Concepts and Techniques
3D Cube Browser
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Data Mining: Concepts and Techniques

13. Data Mining: Concepts and Techniques
Current Status
Evolving to DBMiner 3.0
Smooth integration of relational database and data warehouse systems
Support Microsoft OLEDB for Data Mining
Integrates naturally with Microsoft SQLServer 2000 Analysis Service, as one of Microsoft SQLServer 2000 Analysis Service providers
Adding fast association mining, sequential pattern mining and gradient mining methods
Adding predictive associative classification method
Towards RetailMiner, WebMiner, GeoMiner, and Bio-Miner
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Data Mining: Concepts and Techniques

14. Data Mining: Concepts and Techniques
Contact
For licensing, purchasing and other issues
Please consult and contact
Welcome application-oriented in-depth development contract
Welcome R&D collaborations, joint research and development, technology licensing, and product/company acquisition
November 9, 2018
Data Mining: Concepts and Techniques

15. Data Mining: Concepts and Techniques
Thank you !!!
November 9, 2018
Data Mining: Concepts and Techniques