DM: Software announcement & Introduction

[Jie Cheng]

              ANNOUNCEMENT
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========
A belief network learning system is now available for download. It 
includes
a wizard-like interface and a construction engine.

Name: Belief Network Power Constructor
Version: 1.0 Beta 1
Platforms: 32-bit windows systems (windows95/NT)
Input: A data set with discrete values in the fields (attributes) and
optional domain knowledge (attribute ordering, partial ordering, 
direct
causes and effects).
Output: A network structure of the data set. 

Main Features:

1.Easy to use. It gathers necessary input information through 5 simple
steps.

2.Accessibility. Supports most of the popular desktop database and
spreadsheet formats, including: Ms-Access, dBase, Foxpro, Paradox, 
Excel
and text file formats. It also supports remote database servers like
ORACLE, SQL-SERVER through ODBC. 

3.Reusable. The engine is an ActiveX DLL, so that you can easily 
integrate
the engine into your belief network, datamining or knowledge base 
system
for windows95/NT. 

4.Efficient. This engine constructs belief networks by using 
conditional
independence(CI) tests. In general, it requires CI tests to the 
complexity
of O(N^4); when the attribute ordering is known, the complexity is 
O(N^2).
N is the number of attributes (fields).

5.Reliable. Modified mutual information calculation method is used as 
CI
test to make it more reliable when the data set is not large enough. 

6.Support domain knowledge. Complete ordering, partial ordering and 
causes
and effects can be used to constrain the search space and therefore 
speed
up the construction process. 

7.Running time is Linear to the number of records. 


The system can be downloaded from web site:
http://193.61.148.131/jcheng/bnpc.htm

Suggestions and comments are welcome.

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Hi all, 
I would also like to introduce myself.
I am a final year Ph.D. student in the field of datamining. My main
interest is in Belief network learning from database. I have just 
finished
the above system, which is based on my belief network learning 
algorithm. I
hope you can download and try it. Your comments and suggestions will 
help
me to improve this system and my Ph.D. thesis.
After I finish my study, I'd like to have a research or datamining 
system
development position where I can continue my work in this field. Since
belief net learning (automated graphical modeling) techniques are 
already
mature, I think it is time to implement them into the real world 
datamining
systems.

Regards,

Jie Cheng
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Jie Cheng       email: j.cheng@ulst.ac.uk
16J24, Faculty of Informatics, UUJ, UK. BT37 0QB
Tel: 44 1232 366500 Fax: 44 1232 366068
http://193.61.148.131/jcheng/
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