RE: DM: Datamining Definition.

Hello,

       I tend to agree with Werner. His definition is closer to my
definition. However, I disagree that you need large and messy data sets to
do data mining.

     The main purpose of data mining is to maximize profit and minimize
risks. The methodology is immaterial whether it is based on Statistics, OR,
AI/ML or any other technique. The methodology could be formal or informal.
The main goal is to be valuable to the business. Data mining goes much
further than just theoretical methodology and includes driving  towards the
main objective of maximizing profits and minimizing risks.

     I, therefore, define data mining as a formal/informal process of
extracting "valuable information" from the data sets to maximize profit and
minimize risks. In most cases these data sets are large and messy.

Narayan Bhalerao
Data Mining Manager
Westpac Banking Corporation
Level 6, 341 George Street
Sydney 2000 NSW Australia
Phone: 61-2-9220-3706
E Mail: nbhalerao@westpac.com.au



 > ----------
 > From: 	Werner E. HELM[SMTP:helm@fh-darmstadt.de]
 > Reply To: 	datamine-l@nautilus-sys.com
 > Sent: 	Friday, 31 March 2000 21:33
 > To: 	datamine-l@nautilus-sys.com
 > Subject: 	Re: DM: Datamining Definition.
 >
 >
 > Hi all :
 >
 > Warren Sarle wrote :   .....
 >
 > So I choose to define data mining as the application of statistical
 > decision theory to huge, messy data sets to maximize profits.
 >
 > Well, coming myself from a background of Statistics and Operations
 > Research, I could agree.
 > However, it depends on how wide or narrow you understand "statistics".
 >
 > A bit wider would be :
 >
 > " So I choose to define data mining as the application of any formal
 > method
 > around
 > decision theory to huge, messy data sets to maximize profits."
 >
 > Formal method would comprise Statistics, Operations Research,AI/ML-methods
 >
 > and maybe future concepts from Mathematics.
 >
 > Since I very seldom have experienced that the results of decision theory
 > go
 > to decisions without human interaction of some kind of management guy, I
 > could return to "decision support" and suggest :
 >
 > " So I choose to define data mining as any type of decision support in all
 >
 > cases of huge, messy data sets to maximize profits."
 >
 > Of course decision support methods would mainly come from  Statistics,
 > Operations Research,AI/ML-methods and maybe future concepts from
 > Mathematics.
 >
 >
 > But :  A sales argument needs no definition.
 > But :  A strong movement will find it's way on campus, attached to
 > CS-dept., Stats-dept., OR-dept.  or between existing disciplines. If
 > strong
 > and persistent enough it will create an own existence.
 >
 > So let's wait and see how strong and persistent  DM  will be when the
 > sales
 > people move on !!
 >
 >
 > Werner E. HELM .
 > 

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