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Siftware: DBMiner
*URL:
http://db.cs.sfu.ca/DBMiner
*Description:
DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational dtabases. It is based on our studies of data mining techniques and our experience in the development of an early system prototype, DBLearn. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance.
*Discovery tasks: Classification, Summarization, Dependency analysis, Visualization, Prediction, Class Comparison
*Comments:
The system has the following distinct features:
It incorporates several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining
multiple-level rules and meta-rule guided knowledge mining, etc., and implements
a wide spectrum of data mining functions including generalization, characterization, association, classification, and prediction.
It performs interactive data mining at multiple concept levels on any user-specified set of data in a database using an SQL-like Data Mining Query Language, DMQL, or a graphical user interface. Users may interactively set and adjust various thresholds, control a data mining process, perform roll-up or drill-down at multiple concept levels, and generate different forms of outputs, including generalized relations, generalized feature tables, multiple forms of generalized rules, visual presentation of rules, charts, curves, etc.
Efficient implementation techniques have been explored using different data structures, including generalized relations and multiple-dimensional data cubes, and being integrated with relational database techniques. The data mining process may utilize user- or expert-defined set-grouping or schema-level concept hierarchies which can be specified flexibly, adjusted dynamically based on data distribution, and generated automatically for numerical attributes.
Both UNIX and PC (Windows/NT) versions of the system adopt a client/server architecture. The latter communicates with various commercial database systems for data mining using the ODBC technology.
*Platform(s): Windows (95, NT), Unix
*Contact:
Jiawei Han
School of Computing Science
Simon Fraser University
Burnaby, B.C
Canada V5A 1S6
tel: (604)291-4411
fax: (604)291-3045
email: han@cs.sfu.ca
*Status: Research Prototype
*Source of information: DBLab of SFU
*Updated: 1996-09-06 by Wan Gong, wgong@cs.sfu.ca