AW: DM: Datamining Definition.

Defining DM , the Xth
I feel not comfortable with all those definitions, so that's my try:

For definitions we need objectives. In Data-Mining we apply some methods
which should extract some insides out of  data which relates to the
objectives of a specific enterprise....

Maximizing Profits:
To simplify  business objectives to maximum profit, isn't a sensible
definition from a managerial view. Profit is a short-term - term.
Shareholder Value (SV) is a long-term one. But maximizing SV is a) a complex
issue and b) do not guaranty a successful future. There are Stakeholder
(e.g. moral of the public ... (look at doubleclick...) ) etc witch have to
be considered ...  So , this is just to complex for an definition.
Lets better use instead of "maximizing profit" , "solve objectives which was
derived from the management".

Methods
There are many scientific field from which we could derive some DM-Methods.
I personally cant distinguish all the methods from there origin. This is
also not our cup of tea. Let us describe the methods from there objectives
too. What should this methods do?
For me, the best descriptions of what DM-Methods do is "Learning", they
examine inductive reasoning from Data.
They do no confirmatory statistics and do no deductive reasoning.

So here is my suggestion:

DM is: "Acquiring knowledge (laws and regularities) automatically from data
in order to achieve an business objective of an enterprise."


Frank Buckler
   University Hanover
   Department Marketing II
Buckler@m2.uni-hannover.de



-----Ursprüngliche Nachricht-----
Von: owner-datamine-l@nautilus-sys.com
[mailto:owner-datamine-l@nautilus-sys.com]Im Auftrag von BHALERAO, Narayan
Gesendet: Montag, 3. April 2000 07:08
An: datamine-l@nautilus-sys.com
Betreff: 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 .
  >

• Prev by Date: DM: RE: DM :Making newsgroup for DM in usenet?
• Next by Date: Re: DM: PhD?
• Prev by thread: Re: DM: Data Mining Software Market and Data Mining Vendor's Market Share
• Next by thread: DM: RE: DM :Making newsgroup for DM in usenet?
• Index(es):
  • Date
  • Thread