Download No Slide Title

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
http://www.cs.sfu.ca
May 22, 2017
Data Mining: Concepts and Techniques
1
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
May 22, 2017
Data Mining: Concepts and Techniques
2
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
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
May 22, 2017
Data Mining: Concepts and Techniques
4
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)
May 22, 2017
Data Mining: Concepts and Techniques
5
DBMiner Data and Mining Views (Working Panel)
May 22, 2017
Data Mining: Concepts and Techniques
6
OLAP (Summarization) Display Using MS/Excel 2000
May 22, 2017
Data Mining: Concepts and Techniques
7
Market-Basket-Analysis (Association)—Ball graph
May 22, 2017
Data Mining: Concepts and Techniques
8
Display of Association Rules in Rule Plane Form
May 22, 2017
Data Mining: Concepts and Techniques
9
Display of Decision Tree (Classification Results)
May 22, 2017
Data Mining: Concepts and Techniques
10
Display of Clustering (Segmentation) Results
May 22, 2017
Data Mining: Concepts and Techniques
11
3D Cube Browser
May 22, 2017
Data Mining: Concepts and Techniques
12
Current Status

Evolving from DBMiner2.0 to DBMiner2.5





Smooth integration of relational database and data
warehouse systems
Support Microsoft OLEDB for Data Mining
Adding fast association mining and sequential pattern
mining methods
Adding visual classification methods
Towards RetailMiner, WeblogMiner, WebMiner, GeoMiner,
MultiMediaMiner, and DNAMiner
May 22, 2017
Data Mining: Concepts and Techniques
13
Contact

90 day trial use of DBMiner:


For license purchasing and other issues



DBMiner 2.0 is downloadable at www.dbminer.com
Please consult and contact www.dbminer.com
Welcome application-oriented in-depth development
contract
Welcome R&D collaborations, joint research and
development, technology licensing, and
product/company acquisition
May 22, 2017
Data Mining: Concepts and Techniques
14
http://www.cs.sfu.ca/~han/dmbook
Thank you !!!
May 22, 2017
Data Mining: Concepts and Techniques
15