RE: DM: RE: Data Forms for Mining (Limit on variables)

Under what assumptions?

Most real world datasets contain a lot of highly correlated variables. So
the underlying probability distribution of the data is much, much lower. We
do a lot of datamining on surveys with 5k-10k attribute value pairs and
around 10k of records; subselections contain al lot less records ofcourse.

In my point of view algorithms should be able to cope with this kind of
data, because ideally you don't want to force the user to do this
datapreparation. And for descriptive datamining it even makes sense to
include variables which are the same from a statistical point of view, but
conceptually different for the user. There *is* a lot to gain from
datapreparation, but this should be automized as much as possible
(forward/backward selection)

Any reactions?

Peter

 > -----Original Message-----
 > From: Frank Buckler [mailto:buckler@m2.uni-hannover.de]
 > Sent: Wednesday, May 24, 2000 11:15 AM
 > To: datamine-l
 > Subject: AW: DM: RE: Data Forms for Mining (Limit on variables)
 >
 >
 >
 > Issue: Number of Inputs
 >
 > I'm surprised to hear that some guy's are using from thousand
 > up to million
 > of inputs.
 >
 > There exist an upper bound on input-number determined by
 > sample size. This
 > is advocated due to the VC-Dimension.
 > For linear regression you need n+1 examples in sample (n =
 > number of input)
 > This is true but much more severe for non-linear modelling!
 >
 > frank
 >
 > -----Ursprüngliche Nachricht-----
 > Von: owner-datamine-l@nautilus-sys.com
 > [mailto:owner-datamine-l@nautilus-sys.com]Im Auftrag von
 > greg.della-croce@marchFIRST.com
 > Gesendet: Dienstag, 23. Mai 2000 15:01
 > An: datamine-l@nautilus-sys.com
 > Betreff: Re: DM: RE: Data Forms for Mining
 >
 >
 > Eric,
 >        I think you miss read the line.  It is 700 Variables, not
 > characters.   But
 > that does bring up an interesting question.  IF DM uses a 0NF
 > to lay out
 > data
 > (all the info for any given instance in one record)  What are
 > the practical
 > limits of the tool, today, for the number of variables in
 > that record.    I
 > am
 > currently working with biomed field and that could very
 > easily go into the
 > hundreds of varables per instance.
 >        Anyone have any input on this?
 >
 > Greg
 >
 >
 >
 >
 > Eric Bloedorn <bloedorn@mitre.org> on 05/22/2000 12:17:37 PM
 >
 > Please respond to datamine-l@nautilus-sys.com
 >
 > To:   datamine-l@nautilus-sys.com
 > cc:    (bcc: Greg Della-Croce/Whittman-Hart LP)
 > Subject:  Re: DM: RE: Data Forms for Mining
 >
 >
 >
 >
 >
 > Ken: I am curious - what commercial tools choke and die on
 > tables wider
 > than 700 chars?!
 >
 > -Eric Bloedorn,
 > MITRE Corporation
 >
 > "Collier, Ken" wrote:
 >    >
 >    > This question hit a hot-button for me. While most OLAP and DSS
 > technology
 >    > require a great deal of structure in thier data (e.g.,
 > start schema),
 > data
 >    > mining tools expect the data to be denormalized into a
 > single 2D table.
 >    > Furthermore, aside from market basket analysis, most data mining
 > algorithms
 >    > assume that each observation in a data set represents a
 > unique entity
 > (e.g.,
 >    > each record is a different customer).
 >    >
 >    > What this implies is that there is substantial data preprocessing
 > required
 >    > in most cases to transform data from a relational, star, or other
 > structured
 >    > model, into the mineable denormalized structure required. In our
 > experience
 >    > with retailers, telecos, manufacturers, insurance
 > companies, banks, and
 >    > others, this preprocessing generally consumes about 80%
 > of the total
 > effort
 >    > compared to the actual data mining, validation, verification, and
 >    > deployment, which consumes the remaining 20%. Your
 > mileage may vary.
 >    >
 >    > Now, here's the rub: We recently had a manufacturing
 > client with ~1000
 >    > quality control parameters for each component within a
 > single widget. In
 >    > this scenario a widget is made up of 2-6 major
 > sub-widgets, and each
 >    > sub-widget is made up of 3 components. The same set of
 > QC parameters is
 >    > collected on each component. So, even when we
 > denormalize the data into
 > a
 >    > single table, there can be as many as 18 (6 x 3) records
 > for a single
 >    > widget. Our objective in this analysis was to identify
 > root causes of
 > widget
 >    > failure in order to reduce the defect rate.
 >    >
 >    > Now, we want the data mining algorithms to "see" all 18 records
 > associated
 >    > w/ a single widget as a single "pattern". Unfortunately
 > commercial tools
 >    > don't tune their algorithms to do this even though it is
 > technically
 >    > possible. One exception is time series and sequence analysis
 > algorithms, but
 >    > these methods are really intended for a different
 > purpose. Another
 > kludgy
 >    > solution to this problem is to string out all 18 records
 > into a single
 > WIDE
 >    > record per widget. Many commercial tools choke and die
 > on tables that
 > are
 >    > wider than 700 vars.
 >    >
 >    > We finally wound up solving this problem using SAS
 > Enterprise Miner and
 > SGI
 >    > Mineset, but not without a lot of data transformations,
 > preprocessing,
 > and
 >    > preliminary variable reduction. To my thinking, the next
 > generation of
 > data
 >    > mining tools should provide the flexibility to "see"
 > data in a wide
 > variety
 >    > of structures. The price we may pay for this flexibility
 > is the speed of
 >    > data sourcing prior to analysis.
 >    > ---
 >    > Ken Collier
 >    > Senior Manager, Business Intelligence
 >    > KPMG Consulting
 >    > Corporate Sponsor of the Center for Data Insight
 > http://insight.cse.nau.edu
 >    >
 >    > -----Original Message-----
 >    > From: greg.della-croce@marchfirst.com
 >    > [mailto:greg.della-croce@marchfirst.com]
 >    > Sent: Thursday, May 18, 2000 6:09 AM
 >    > To: datamine-l@nautilus-sys.com
 >    > Subject: DM: Data Forms for Mining
 >    >
 >    > I have worked in and around Data Warehouse/Marts with
 > their star schema
 > for
 >    > awhile now.   However I am interested what form the data
 > takes when it
 > is
 >    > being
 >    > optimized for Mining.   I am speaking to structured data, not
 > unstructured
 >    > data
 >    > such as large bodies of text.   What are the
 > architectures of a Data
 > Mining
 >    > DB?
 >    > Is the form dependent on the algorithms that you are
 > going to employ
 >    > against it?
 >    > Or is it more general in nature?
 >    >
 >    > Thank you for your replies!
 >    >
 >    > Greg Della-Croce
 >    > marchFirst
 >    > BI/KM
 >    >
 >    >
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