KDnuggets : News : 2000 : n21 : item7    (previous | next)

Software

From: Fernando Berzal fberzal@decsai.ugr.es
Date: Tue, 10 Oct 2000 11:45:07 +0200
Subject: Free software: TMiner Personal Edition
Summary: This personal edition of TMiner collects some algorithms
to mine association rules, build classifiers and cluster data obtained
from relational databases using JDBC (the standard Java call-level
interface). This version of TMiner has been tested with different
DBMSs such as Oracle 8i, IBM DB2 UDB and InterBase.

TMiner Personal Edition -- Free Java Data Mining software is downloadable from
    http://frontdb.ugr.es (Research section)

TMINER FACT SHEET
-----------------

System Requirements:

 Any Java 2 Virtual Machine,
 plus the suitable JDBC driver for your database.

 Numerical Cruncher can also be used as a stand-alone
 application accessing data stored in local files
 without the need of JDBC. Check the documentation
 for more details.

TMiner Development Team:

- Fernando Berzal (fberzal@decsai.ugr.es)
- Juan Carlos Cubero (JC.Cubero@decsai.ugr.es)

     Any questions or suggestions
will be welcomed at fberzal@decsai.ugr.es

TMINER DESCRIPTION
------------------

Data Mining is a generic term which covers research results,
techniques and tools used to extract useful information from
large databases.  KDD, which stands for Knowledge Discovery
in Databases, has been defined as the non-trivial extraction
of potentially useful information from a large volume of data
where the information is implicit (although previously unknown).

TMiner collects some techniques which may be useful to interpret
big datasets (stored in relational tables) and discover relationships
among data. This personal edition of TMiner collects some algorithms
to mine association rules, build classifiers and cluster data obtained
from relational databases using JDBC (the standard Java call-level
interface). This version of TMiner has been tested with different
DBMSs such as Oracle 8i, IBM DB2 UDB and InterBase.

TMiner can be used to explore your own datasets and it is also
useful for teaching Data Mining and Pattern Recognition (as its
use can help CS/CE students to understand the behaviour of different
algorithms and their applicability).

KDnuggets : News : 2000 : n21 : item7    (previous | next)

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