hello,,,i neeed your help,,i m studing data mining,,,and i have work to do
about visualisation of data flow,,,so i need some documentations....thank you
for your help and im really sorry because i have some difficulties en english.



[Non-text portions of this message have been removed]

Hi all,

This may be of interest to your educational and professional pursuits.

Regards!

Scott

==========


RAPID PROGRESS MARKETING AND MODELING ANNOUNCES
DATA MINING AND PREDICTIVE MODELING TRAINING COURSES
AND USA SEMINAR CITIES FOR 2009


Totally New Data Mining and Predictive Modeling Training Courses
Are Now Available For Executives and Marketers Who Need To
Effectively Combat Tough Economic Times And Ramp Up Quickly


St. Petersburg, FL, January 5, 2009 - With increasing numbers of
business failures and the harsh financial realities of the current
economy in mind, today, Rapid Progress Marketing and Modeling, LLC
(RPM˛) announced a relevant new series of data mining and predictive
modeling training courses for Internet Marketers and Direct Marketers.
Concurrent with the announcement, RPM˛ has released its 2009 schedule
of training course dates for Tampa Bay, Dallas, Houston, Atlanta and
Miami.

The new courses have been tailored for today's economic environment,
equipping executives and practitioners with the most current and
impactful techniques that can be used to reduce costs and grow new
revenue streams.  As part of the format, special emphases have been
placed on minimizing technical jargon, providing experienced, practical
advice, and rapid implementation.  Both "Essentials" and
"Advanced" classes will be offered consecutively at scheduled
locations as well as onsite or at sponsored locations.

Scott Terry, President of RPM˛ and a 30 year industry veteran said,
"We believe the timing of courses relevant to the state of market
conditions could not be more important.  Not only are sound data mining
practices providing a key competitive edge for businesses, they're
even more important in difficult times like these.  Personal, hands-on
training has proven to be the best and quickest means for getting up to
speed and making an ongoing impact."

Based in St. Petersburg, Florida, Rapid Progress Marketing and Modeling,
LLC is a network of senior executives and specialists who provide data
mining and predictive modeling services, professional training, and
marketing services for internet, direct, and target marketers.

For More Information
Data Mining Training:
http://www.rpmsquared.com/data-mining-training.html
<http://www.rpmsquared.com/data-mining-training.html>
Essentials Class:
http://www.rpmsquared.com/essentials-data-mining-training.html
<http://www.rpmsquared.com/essentials-data-mining-training.html>
Advanced Training:
http://www.rpmsquared.com/advanced-data-mining-course.html
<http://www.rpmsquared.com/advanced-data-mining-course.html>



[Non-text portions of this message have been removed]

Hi everyone,

Predictive Analytics World's program for Feb 18-19 in San Francisco
(www.predictiveanalyticsworld.com) has grown a bit since my post
several weeks ago, and is looking better than ever.  The conference
covers today's commercial deployment of predictive analytics, across
industries and across software vendors. In a nutshell, PAW is a
warehouse of case studies.

In breaking news, the Netflix Prize leader slated to speak at PAW has
now won the Netflix Progress Prize (see
www.predictiveanalyticsworld.com/agenda.php#advancedapproaches)

We have two predictive analytics workshops that serve as a third-day
option on Feb 20 to complement the core conference program:

"The Best and the Worst of Predictive Analytics: Predictive Modeling
Methods and Common Data Mining Mistakes"
Instructor: John F. Elder, Ph.D., CEO and Founder, Elder Research, Inc.
www.predictiveanalyticsworld.com/predictive_modeling_methods.php

"Taking Action with Analytics: The Decision-Centric Enterprise -
Putting Predictive Analytics to Work Improving Every Business Decision"
Instructor: James Taylor, Co-Founder, Smart (enough) Systems
www.predictiveanalyticsworld.com/decision_centric_enterprise.php

And the list of case studies has expanded - we now have case studies
from all of: 3M, Acxiom, Affiliated Computer Services, Charles Schwab,
Click Forensics, Google, Linden Lab (Second Life), The National Rifle
Association, Pinnacol Assurance, Reed Elsevier, San Diego
Supercomputer Center, Sun, Telenor, Wells Fargo Credit Card Services,
Wells Fargo Internet Services Group -- plus special examples from
Anheuser-Busch, Disney, Hewlett-Packard, HSBC, IRS, Pfizer, Social
Security Administration and WestWind Foundation.

For more info about the program, see:
www.predictiveanalyticsworld.com

Be sure to register by January 18th for the Early Bird rate - save
$200 at:
www.predictiveanalyticsworld.com/register.php

If you'd like our informative event updates, sign up at:
www.predictiveanalyticsworld.com/notifications.php

For a summary of business applications of predictive analytics - and a
named case study for each - see the article, "Predictive Analytics
Delivers Value Across Business Applications" at
www.predictiveanalyticsworld.com/businessapplications.php

What is predictive analytics? See the Predictive Analytics Guide:
www.predictiveanalyticsworld.com/predictive_analytics.php

Take the Predictive Analytics World survey (on applications)
As predictive analytics quickly expands across verticals and
applications, we need your help to understand what the evolving
landscape looks like. Please take a few minutes, answer a handful of
questions, and help us keep you informed:
https://www.surveymonkey.com/s.aspx?sm=8dHx_2bFz7yxw3FPKlbi3OVg_3d_3d

Available for immediate download: Free Industry Survey (on tools)
Download the Rexer Analytics 2008 Data Miner Survey report, covering
the most popular software tools, which verticals have embraced
modeling and more, at:
http://www.predictiveanalyticsworld.com/survey-signup.php

And finally, let us know if you have any questions.

Best,
Elise Johnson
Predictive Analytics World

-- CALL FOR PARTICIPATION - Deadline for applications: 30 January
2009 --

  Postgraduate Summer School on Computer Science and Information
Systems 2009
  Algarve, Portugal, 17 to 23 June 2009
  (http://www.mccsis.org/school/)
  parallel with IADIS Multi Conference on Computer Science and
Information
  Systems  (MCCSIS 2009)
  Algarve, Portugal, 17 to 23 June 2009
  (http://www.mccsis.org)

  * Who can apply
  The summer school is open to all participants enrolled in a PhD or
doctoral course or in a master degree course, or recently having
completed their
  postgraduate degrees in a higher education institution. There will
be two classes, one with doctoral degree students and another with
master students.

  * Candidates for the summer school are expected to
  - hold a master or baccalaureate degree in the areas of computer
science or information systems for or an appropriate degree for the
Summer Schools areas,
  - have a previous knowledge in at least one area of the keynote
lectures or the areas covered by the MCCSIS conference,
  - demonstrate their ability to work in the field as researcher /
student

  The application process is governed by the rules determined and
approved by the organizing committee of the summer school. The
evaluation of the
  applications will be guaranteed by the organizing committee. The
organizing committee will select the most promising candidates among
the applicants.

  * Format of the Summer School
  The Summer School will be held in conjunction with the Multi-
conference on Computer Science and Information Systems (MCCSIS 2008).
The registration
  fee from the Summer School will give access to
  - Session on research methods;
  - Session on the candidate's doctoral or master projects analysis
and discussion;
  - Keynotes from the conference keynote speakers in the selected
areas ( 11 confirmed until now);
  - Technical sessions from the MCCSIS conference;
  - Tutorials from the conference (to be announced).

  * How to apply
  Applications following the rules specified on the application form
should be sent through http://www.mccsis.org/school/

  * Important dates
  - Submission of Application - until 30 January 2009
  - Notification of acceptance - Until 6 March 2009
  - Registration - Until 6 April 2009
  - Late Registration: After 6 April 2009
  - Postgraduate Summer School - 17 to 23 June 2009

  * Secretariat
  IADIS Secretariat - Postgraduate Summer School on Computer Science
and Information Systems 2009
  Rua Sao Sebastiao da Pedreira, 100, 3
  1050-209 Lisbon, Portugal
  E-mail: school@...
  Web site: http://www.mccsis.org/school/

  * Scientific Advisors
Miguel Baptista Nunes, University of Sheffield, United Kingdom
Piet Kommers, University of Twente, The Netherlands
Nian-Shing Chen, National Sun Yat-sen University, Taiwan
Pedro Isaías, Universidade Aberta (Portuguese Open University),
Portugal

-- CALL FOR PARTICIPATION - Deadline for applications: 30 January
2009 --

  Postgraduate Summer School on Computer Science and Information
Systems 2009
  Algarve, Portugal, 17 to 23 June 2009
  (http://www.mccsis.org/school/)
  parallel with IADIS Multi Conference on Computer Science and
Information
  Systems  (MCCSIS 2009)
  Algarve, Portugal, 17 to 23 June 2009
  (http://www.mccsis.org)

  * Who can apply
  The summer school is open to all participants enrolled in a PhD or
doctoral course or in a master degree course, or recently having
completed their
  postgraduate degrees in a higher education institution. There will
be two classes, one with doctoral degree students and another with
master students.

  * Candidates for the summer school are expected to
  - hold a master or baccalaureate degree in the areas of computer
science or information systems for or an appropriate degree for the
Summer Schools areas,
  - have a previous knowledge in at least one area of the keynote
lectures or the areas covered by the MCCSIS conference,
  - demonstrate their ability to work in the field as researcher /
student

  The application process is governed by the rules determined and
approved by the organizing committee of the summer school. The
evaluation of the
  applications will be guaranteed by the organizing committee. The
organizing committee will select the most promising candidates among
the applicants.

  * Format of the Summer School
  The Summer School will be held in conjunction with the Multi-
conference on Computer Science and Information Systems (MCCSIS 2008).
The registration
  fee from the Summer School will give access to
  - Session on research methods;
  - Session on the candidate's doctoral or master projects analysis
and discussion;
  - Keynotes from the conference keynote speakers in the selected
areas ( 11 confirmed until now);
  - Technical sessions from the MCCSIS conference;
  - Tutorials from the conference (to be announced).

  * How to apply
  Applications following the rules specified on the application form
should be sent through http://www.mccsis.org/school/

  * Important dates
  - Submission of Application - until 30 January 2009
  - Notification of acceptance - Until 6 March 2009
  - Registration - Until 6 April 2009
  - Late Registration: After 6 April 2009
  - Postgraduate Summer School - 17 to 23 June 2009

  * Secretariat
  IADIS Secretariat - Postgraduate Summer School on Computer Science
and Information Systems 2009
  Rua Sao Sebastiao da Pedreira, 100, 3
  1050-209 Lisbon, Portugal
  E-mail: school@...
  Web site: http://www.mccsis.org/school/

  * Scientific Advisors
Miguel Baptista Nunes, University of Sheffield, United Kingdom
Piet Kommers, University of Twente, The Netherlands
Nian-Shing Chen, National Sun Yat-sen University, Taiwan
Pedro Isaías, Universidade Aberta (Portuguese Open University),
Portugal

[We apologize for multiple copies]

======================= CALL FOR PAPERS ============================

                10th International Conference on
             Electronic Commerce and Web Technologies
                         EC-Web 09

               http://sisinflab.poliba.it/ecweb09/

               Johannes Kepler University of Linz
                       Linz, Austria
                 31 August - 4 September 2009

====================================================================

EC-Web 2009

After the initial enthusiastic initiatives and investments and the eventual
bubble, Electronic Commerce has changed and evolved in a well established
and founded reality both from a technological point of view and from a
scientific one. Nevertheless, together with its evolution, new challenges
and topics have emerged as well as new questions have raised related to many
aspects of Electronic Commerce.

After the lesson learned during last years, for its 10th edition EC-Web
completely renews its structure trying to provide a clearer description of
the Electronic Commerce universe focusing on some relevant topics. The main
focus is not only on Internet related techniques and approaches. The aim of
EC-Web 2009 is to cover also aspects related to theoretical foundation of
E-Commerce, Business Processes as well as new approaches exploiting recently
emerged technologies and scenarios such as Semantic Web, Web services, SOA
architectures, mobile and ubiquitous computing, just to cite a few. Due to
their central role in any realistic e-commerce infrastructure security and
privacy issues are widely considered, without excluding legal and regulatory
aspects.

We encourage papers that present innovative solutions for classical
E-Commerce issues, such as e-negotiation, recommender systems, secure
payments, as well as for new emerging areas, such as the Semantic Web,
applied Web services, applied soft computing and information retrieval
techniques in web and/or e-commerce environments.

Although the conference seems naturally focused on computer science issues,
we welcome and encourage research contributions from economics, business
administration, law, sociology and other disciplines. In particular, papers
about web marketing and its non-technical aspects, as well as about social
aspects of e-commerce are encouraged. We also welcome papers reporting about
innovative applications and case studies in the field of E-Commerce and/or
Web Applications, like, e.g., Electronic Voting and Sponsored web search.

In order to provide a better categorization and classification of its main
goals, Ec-Web 2009 will be organized as a multi-track conference. Each track
will be organized as a mini-conference with corresponding reference area
chairs. Even though the main focus of the conference concerns the
relationship between E-commerce and emerging technologies, with strong
attention toward semantics aspects, contributions about theoretical issues
as well as cross-track contributions are very welcome.


TRACKS

* Service Oriented E-Commerce and Business Process
     o Business process modeling and analysis
     o Software architectures
     o Processes and service composition
     o Business process and e-service repositories
     o Quality of Service in business processes
     o Security in business processes
     o Cross-organizational process support, contracts
     o Workflow management systems
     o Process modeling and enactment in ERP, CRM, and SCM systems
     o SOA and Process Management
     o Resource management in business process execution
     o Enterprise Application Integration
     o Inter-organizational Systems
     o SOA approaches to E-Commerce
     o Virtual enterprises, supply chains, coalitions

* Recommender Systems
     o Innovative applications of recommender technology
     o Recommendation learning and reasoning
     o Industrial application of recommendation technology
     o Recommender system user interfaces and conversational approaches
     o Explanations in recommender systems
     o Group recommender systems
     o Consumer decision making and consumer buying behavior models
     o Collaborative Filtering
     o Appraisal of Recommender Systems
     o User Issues in Recommender Systems
     o Recommendation Interfaces
     o Computational advertising
     o Decision theory and preferences

* E-Payment, Security and Trust
     o Payment and authentication protocols
     o Micropayments
     o Access Control
     o Privacy-enhancing technologies
     o Information Hiding and Watermarking
     o Reputation and trust systems
     o Security and privacy issues in electronic commerce
     o E-Commerce Dependability
     o Transactions and Contracts
     o Legal and Regulatory Issues
     o Electronic voting

* Electronic Commerce and Web 3.0
     o Ontology engineering for E-Commerce
     o Taxonomies and Web directories for semantic E-Commerce
     o Semantic-based discovery of resources in a marketplace
     o Semantic electronic markets
     o Semantic Web Service architecture for E-Commerce
     o Semantic E-Procurement
     o Ontology-based user profiling
     o Application of standard and non-standard reasoning in E-Commerce
     o User tagging for item annotation and discovery
     o Cloud computing in E-Commerce scenarios
     o User interface to exploit Web 3.0 technologies in E-Commerce
     o Emerging languages for E-Commerce
     o Mobile and Pervasive Commerce
     o P2P E-Commerce
     o Experience with e-commerce systems
     o Usability of e-commerce systems


CONFERENCE CHAIRS

* Francesco Buccafurri - Universitŕ Mediterranea di Reggio Calabria
* Tommaso Di Noia - Politecnico di Bari


TRACK CHAIRS

Service Oriented E-Commerce and Business Process

* Stefan Tai - Karlsruhe University

Recommender Systems

* Giovanni Semeraro - Universitŕ degli Studi di Bari

E-Payment, Security and Trust

* Barbara Masucci - Universitŕ di Salerno

Electronic Commerce and Web 3.0

* Martin Hepp - Bundeswehr University Munich


IMPORTANT DATES

- Submission of abstracts: March 16, 2009
- Submission of full papers: March 21, 2009
- Notification of acceptance: May 15, 2009
- Camera-ready copies due: June 10, 2009



SUBMISSION

Authors are invited to submit original research contributions in English,
following the LNCS format (http://www.springer.de/comp/lncs/authors.html).
Duplicate submissions are not allowed and will be rejected Papers should not
exceed 12 pages (i.e. 12 LNCS formatted pages). Submitted papers will be
carefully evaluated based on originality, significance, technical soundness,
and clarity of exposition. All accepted papers will be published in "Lecture
Notes in Computer Science" (LNCS) by Springer Verlag. At least one author is
required to attend the conference and present the paper. For paper
registration, electronic submission and further information please see
http://sisinflab.poliba.it/ecweb09/
and http://www.dexa.org.

==============================================================
      C A L L    F O R    S P E C I A L   S E S S I O N S
==============================================================

DMIN'09 - The 5th International Conference on Data Mining 2009
                    www.dmin--2009.com

          Call for special sessions & workshops

                         part of
         The 2009 World Congress in Computer Science,
         Computer Engineering, and Applied Computing
                      (WORLDCOMP'09)

      Date and Location: July 13-16, 2009, Las Vegas, USA

==============================================================

The 2009 International Conference on Data Mining (DMIN’09) is interested in
offering special sessions, workshop sessions and tutorials on targeted areas
within the field of data mining.

Each technical session should have at least 4 paper presentations while each
workshop should have at least 8 paper presentations. The session chairs will be
responsible for all aspects of their sessions, including soliciting papers,
reviewing, and chairing the technical session on-site during the conference. The
names of session chairs will also appear as Associate Editors in the conference
proceedings and on the cover of the books.

Proposals to organize technical sessions should include the following
information:

- Name and address (+ E-mail) of proposer.
- Title of session
- An indication whether you are attempting a workshop or technical session
- A 100-word description of the topic of the session
- A short description on how the session will be advertised

Proposals for special sessions & workshops should be submitted by email by the
February 7, 2009 deadline to:

Gary M. Weiss
Special Session Chair
special-session-chair@dmin--2009.com


IMPORTANT DATES

    February 7, 2009:  Proposals for organizing/chairing sessions/workshops
    Feb. 25, 2009:     Submission of papers (about 5 to 7 pages)
    March 25, 2009:    Notification of acceptance
    April 25, 2009:    Camera-Ready papers and Registration due
    July 13-16, 2009:  The 2009 International Conference on Data Mining (DMIN'09)
    URL WORLDCOMP:     http://www.world-academy-of-science.org/worldcomp09/ws
    URL DMIN'09:       http://www.dmin--2009.com


ADDITIONAL CONTACT INFORMATION

General Enquiries: Robert Stahlbock
General Conference Chair
conference-chair@dmin--2009.com

Programme Committee: Sven F. Crone
Conference Programme Co-Chair
programme-chair@dmin--2009.com

Student Funding Enquiries:  Stefan Lessmann
Student Chair & Conference Programm Co-Chair
student-chai@dmin--2009.com

Tutorials Session Proposals: Philippe Lenca
Tutorial Chair
tutorial-chair@dmin--2009.com

Special Session Proposals: Gary M. Weiss
Special Session Chair
special-session-chair@dmin--2009.com

Exhibitors & Corporate Sponsors : Wolfram Lippe
Exhibit Chair
exhibits-chair@dmin--2009.com

Hello,

This email message is a notification to let you know that
a file has been uploaded to the Files area of the datamining2
group.

   File        : /USU - DM Research Project /Questionnaire for MF CompaniesV5.doc
   Uploaded by : josedmontero <josedmontero@...>
   Description : Questionnaire of DM Research Survey

You can access this file at the URL:
http://groups.yahoo.com/group/datamining2/files/USU%20-%20DM%20Research%20Projec\
t%20/Questionnaire%20for%20MF%20CompaniesV5.doc

To learn more about file sharing for your group, please visit:
http://help.yahoo.com/l/us/yahoo/groups/original/members/web/index.htmlfiles

Regards,

josedmontero <josedmontero@...>

Hello,

This email message is a notification to let you know that
a file has been uploaded to the Files area of the datamining2
group.

   File        : /USU - DM Research Project
/USU-Approved-Letter-of-Information.doc
   Uploaded by : josedmontero <josedmontero@...>
   Description : USU Institutional Review Board Letter of Information

You can access this file at the URL:
http://groups.yahoo.com/group/datamining2/files/USU%20-%20DM%20Research%20Projec\
t%20/USU-Approved-Letter-of-Information.doc

To learn more about file sharing for your group, please visit:
http://help.yahoo.com/l/us/yahoo/groups/original/members/web/index.htmlfiles

Regards,

josedmontero <josedmontero@...>

Dear Data Mining Group Member


I am a member of the Data Mining Group.  I would like to ask your
help for a research that I am conducting in Data Mining.  This study
is part of my doctoral dissertation at the Department of Management
Information Systems of Utah State University on Analysis of
Applications of Data Mining to Manufacturing.

As part of my research, I will be conducting an online survey among
manufacturing companies in the USA. This survey aims to enhance the
understanding on the factors that affect the adoption of Data Mining
by manufacturing organizations in the USA.  The information gathered
will be very valuable in identifying barriers and drivers for the
successful adoption of Data Mining in all areas of manufacturing
organizations.

I am in the process of examining the language, readability, clarity,
general design, and the face value of the questionnaire. The final
version of this questionnaire will serve as the basis for a web based
survey. I am hoping to have the final on-line version of the survey
posted on-line by the end of January, 2009.

I appreciate very much your help in answering and evaluating the
attached questionnaire. Please feel free to provide to me any
feedback that helps me improving the questionnaire.

If it is possible for you, please let me know if you may help me also
in any of the following areas: 1) Sharing this questionnaire with
some of your contacts that may help to provide a feedback, 2)
Providing to me contact information of professionals in manufacturing
organizations that I may invite to answer the on-line survey.

On folder: USU - DM Research Project you may find the questionnaire
and the Letter of Information of this research from the Institutional
Review Board (IRB) for the protection of human participants at USU


Thank you very much for your help.


Jose D. Montero, Ph.D. Candidate
Work Tel. (435)863-3591
Home Tel. (801)776-5240
E-Mail: JoseDMontero@...

Manager – Customer Marketing Analytics
Base $120k - $125k + 10-15% bonus
For Int'l Scientific Instrument Firm – northern Chicago land, IL
Must be US Citizen, PR, Green Card or TN Visa Holder
Relo allocation; drug screen/background ck conducted

Reports into Director of Direct Marketing
Annual sales of $10 billion; employ 33,000 people in 150 countries
worldwide, and serve over 350,000 customers.  There are 356 people in
the Division of this Company; 52 employed within the Marketing
Department.

Description
The Manager of Customer Marketing Analytics leads and provides
strategic direction to the direct marketing function of the Company
to identify customer analyses that will reveal critical customer
insights needed to develop B2B direct marketing programs to develop
response models, based on customers' behaviors; identifies and
prioritizes customer opportunities.
The successful candidate will also:
• Lead and manage a staff (5 direct reports).  This is a hands-on
position employing data mining techniques to identify appropriate
direct marketing contact strategies for their B2B catalog.  Build and
apply regression-based models for catalog mailing campaigns.
• Participate in establishing business objectives, developing
budgets, and providing summaries of program performance. Ensures a
coordinated global marketing program.
• Maintain awareness of key competitor developments and external best
practices, dynamically adjusting strategy as required.
• Develop processes to ensure critical business needs, to shorten
turnaround time on ad-hoc requests, and to provide deeper analytic
insights to researchers and cross business segment managers.
Requirements

Absolute must have's for this position, B2B within Direct Marketing,
strong and current SAS Programming, expert hands-on Data Mining
skills, strong management experience and strategic development.

• Minimum of 5 years of experience in marketing analytics including
customer modeling, managing specific segment development initiatives
including technical/analytic development process. Must have extensive
knowledge of statistical methods and system design used to analyze
customer data and behavior.
• Must have experience working directly with SAS and programming in
it and other data analytics tools.   (3 of the people that report
into this role are jr. level SAS programmers); Expert in Excel
• Must have B2B experience in a direct marketing environment.
• Strong management experience – 5 direct reports.  This is however a
hands-on lead job.
• Bachelor's degree required in Business Statistics or Marketing –
Graduate degree or MBA a plus
• Must have experience managing creative process to ensure high-
impact customer contacts in a catalog direct mail environment.  B2B
experience is a must!
• Must possess excellent communication, leadership, and
organizational skills to be able to integrate multiple business
needs, build compromise where necessary, and present ideas to senior
management.  Must be expert in developing a strategic plan.
• Must possess strong analytical skills and detail orientation to
interpret analytic findings into relevant and actionable business
insights and to recommend appropriate and effective strategies to
successfully assist in accomplishing business results. Company is
looking to establish methodology and put processes in place – driving
organization around these bylaws.
• Must have communication skills to encourage business to focus on
core customer issues and to produce a clear, concise strategic plan
to assist the organization in meeting its goals.
Position may require up to 10% travel – to occasional conferences



Interested candidates e-mail resume and cover letter to:
Linda@...
Linda Benton, President
Benton Search Associates, Inc.
847.223.8804 www.BentonSearch.com

Dear Data Mining Group Member


I would like to ask your help for a research that I am conducting in
Data Mining.  This study is part of my doctoral dissertation at the
Department of Management Information Systems of Utah State University
on Analysis of Applications of Data Mining to Manufacturing.

As part of my research, I will be conducting an online survey among
manufacturing companies in the USA. This survey aims to enhance the
understanding on the factors that affect the adoption of Data Mining
by manufacturing organizations in the USA.  The information gathered
will be very valuable in identifying barriers and drivers for the
successful adoption of Data Mining in all areas of manufacturing
organizations.

I am in the process of examining the language, readability, clarity,
general design, and the face value of the questionnaire. The final
version of this questionnaire will serve as the basis for a web based
survey. I am hoping to post the on-line version of the survey by the
end of January, 2009.

In our Data Mining Group folder: USU - DM Research Project you may
find the questionnaire and the Letter of Information of this research
from USU - Institutional Review Board (IRB) .

I appreciate very much your help in answering and evaluating the
questionnaire. Please feel free to provide to me any feedback that
helps me improving the questionnaire.

If it is possible for you, please let me know if you may help me also
in any of the following areas: 1) Sharing this questionnaire with
some of your contacts that may help to provide a feedback, 2)
Providing to me contact information of professionals in manufacturing
organizations that I may invite to answer the on-line survey.

Please send e-mail on this request to:
josedmontero@...

Thank you very much for your help.


Jose D. Montero, Ph.D. Candidate
Work Tel. (435)863-3591
Home Tel. (801)776-5240
E-Mail: josedmontero@...

Hello,

This email message is a notification to let you know that
a file has been uploaded to the Files area of the datamining2
group.

   File        : /USU - DM Research Project /Questionnaire for MF
Companies-V5.doc
   Uploaded by : josedmontero <josedmontero@...>
   Description : USU- DM Research Questionnaire

You can access this file at the URL:
http://groups.yahoo.com/group/datamining2/files/USU%20-%20DM%20Research%20Projec\
t%20/Questionnaire%20for%20MF%20Companies-V5.doc

To learn more about file sharing for your group, please visit:
http://help.yahoo.com/l/us/yahoo/groups/original/members/web/index.htmlfiles

Regards,

josedmontero <josedmontero@...>

Hello,

Maybe I can get some help on this site.

I am looking for a clustering algorithm that won't be budged by points
density and will be able to isolate one point as a cluster even when
facing high density groups at other clusters.

I would like to give my example:

assume around 100 points scatter around x=1 y=1
assume another 100 points scatter around point x=1.2 y=1.2
Now add another point (just one) at x=100 y=100

I would like an algorithm that will see such scenario either as a 3
clusters or will see the 200 points as one cluster and the last one as
another cluster.

Most algorithms I tested joined the last point to one of the groups...
even when I increased the number of clusters (I tried even 10), no
algorithm isolated the point.

What type of algorithm will be able to deal with such scenarios?
Note that my real data set is 5D and not 2D as I presented here.
I will be glad to provide the data set if anyone want to play with it.

Appreciate any help on this matter,
Thanks,
tripy.

Dear all,
I have a different variable types in my data set (Continuous,
Categorical and Binary). What is the best way to get the data into the
same scale? and how I deal with each variable type?

Thanks in advance, Surob

Dear all,
I wana check one of the noise clustering algorithm on a noise data set.
can you tell me which data set can be used? and how i can find and down load it?

Best, Hajihashemi 




[Non-text portions of this message have been removed]

can anyone help me in this topic of the data mining (Sequence analysis
algorithm)?..I want resources about that.

many thanks,

Hi
 
My Name is Sheshu,
 
I am a University Student in India. I have completed my M Sc in (Information
Systems).
 
Currently I am pursuing PhD in Computer Sciences. My research dissertation is
Data warehousing and Data Mining Application for Atmospheric studies.
 
My research interests are in Data Warehousing, Data Mining, Business
Intelligence and Atmospheric Sciences. I am focusing on to analyze, integrate
and mange large volume of atmospheric datasets with well defined data
warehousing and data mining query and analysis techniques for effective
scientific decision-supporting and predictive analysis. This would reduce the
complexity analysis of conventional scientific data and applications in the real
world problems. In the future, I would like to continue working on research
problems with the practical implications and I am looking forward to fruitful
collaborations with researchers in cross-disciplinary fields.
 
Accordingly I have gathered 11 Yrs of Global Radiosonde data from World
Meteorological Organization. The data is available for 2 instances in a day like
Hrs 00:00 GMT and 12:00 GMT for various observational level of atmospheric
parameters like Atmospheric Pressure, Geopotential Height, Temperature, Relative
Humidity, Mixing Ratio, Wind Direction, Wind Speed etc…By using of this data I
need to evaluate the atmospheric trends and their uncertainties.
 
 However as beginner I have got bit confusion on my research project and
related studies. There are many queries rolling in my mind like
 
1) How should I start a research project using Data Warehousing/Data Mining
application for above specified 2 instances of the Atmospheric Time Series data?
 
2) How to choose an appropriate method to analyze this type of atmospheric
data? 
 
3) Should I go for either only Data warehousing and Business Intelligence
Techniques or only Data Mining techniques and related mining algorithms?
 
Can any one kindly help me with your inputs on my queries and suggestions on my
research project
 
Many Thanks
 
Sheshu

 




[Non-text portions of this message have been removed]

Hi Everyone,

I am heading a stealth startup based in California and was looking for
some datamining enthusiasts, I am looking for people who have a
passion for coding aka Geeks who have a out of box thinking when it
comes to writing web based applications. It is very essential that the
person has a good understanding of web applications and open source
API's, application mashing is also something that is required. Its not
important that you need to excel in all but you should be atleast
awesome in one of them and be aware of the others, graduate students
who want to be a part of a startup while they are doing their PhD are
welcome too. Please if you are interested contact me at
vt67@... with your resumes or blog or whatever shows your
competence.

Thanks
Vineet Devaiah

Vineet,

That was an intriguing email. My expertise is in data mining and I can double as
a coder. I live in the bay area and if you do too would like to learn more about
the opportunity. Look foward to hearing from you.

Venky




________________________________
From: VINEET <vinaiah@...>
To: datamining2@yahoogroups.com
Sent: Thursday, February 5, 2009 2:23:00 AM
Subject: [Data Mining] Data mining geek needed....


Hi Everyone,

I am heading a stealth startup based in California and was looking for
some datamining enthusiasts, I am looking for people who have a
passion for coding aka Geeks who have a out of box thinking when it
comes to writing web based applications. It is very essential that the
person has a good understanding of web applications and open source
API's, application mashing is also something that is required. Its not
important that you need to excel in all but you should be atleast
awesome in one of them and be aware of the others, graduate students
who want to be a part of a startup while they are doing their PhD are
welcome too. Please if you are interested contact me at
vt67@cornell. edu with your resumes or blog or whatever shows your
competence.

Thanks
Vineet Devaiah







[Non-text portions of this message have been removed]

Hi Venky,

Thanks for the interest, can you send me a CV/resume so that I can have a
look and get  back to you. Let me know if you have any specific questions.

Regards,
Vineet

On Thu, Feb 5, 2009 at 7:36 AM, ramasubbu venkatesh <rvenky4@...>wrote:

>   Vineet,
>
> That was an intriguing email. My expertise is in data mining and I can
> double as a coder. I live in the bay area and if you do too would like to
> learn more about the opportunity. Look foward to hearing from you.
>
> Venky
>
> ________________________________
> From: VINEET <vinaiah@... <vinaiah%40gmail.com>>
> To: datamining2@yahoogroups.com <datamining2%40yahoogroups.com>
> Sent: Thursday, February 5, 2009 2:23:00 AM
> Subject: [Data Mining] Data mining geek needed....
>
> Hi Everyone,
>
> I am heading a stealth startup based in California and was looking for
> some datamining enthusiasts, I am looking for people who have a
> passion for coding aka Geeks who have a out of box thinking when it
> comes to writing web based applications. It is very essential that the
> person has a good understanding of web applications and open source
> API's, application mashing is also something that is required. Its not
> important that you need to excel in all but you should be atleast
> awesome in one of them and be aware of the others, graduate students
> who want to be a part of a startup while they are doing their PhD are
> welcome too. Please if you are interested contact me at
> vt67@cornell. edu with your resumes or blog or whatever shows your
> competence.
>
> Thanks
> Vineet Devaiah
>
> [Non-text portions of this message have been removed]
>
>
>


[Non-text portions of this message have been removed]

Dear Data Mining Group Member,





I would like appreciate very much your help in answering an online
survey at http://www.zoomerang.com/Survey/?p=WEB228PJJH4JYT
<http://www.zoomerang.com/Survey/?p=WEB228PJJH4JYT>



This survey is part of a research that I am conducting on Analysis of
Applications of Data Mining to Manufacturing. This study is part of my
doctoral dissertation at the Department of Management Information
Systems of Utah State University on Analysis of Applications of Data
Mining to Manufacturing.



This survey aims to enhance the understanding on the factors that affect
the adoption of Data Mining by manufacturing organizations in the USA.
The information gathered will be very valuable in identifying barriers
and drivers for the successful adoption of Data Mining in all areas of
manufacturing organizations.



This research has been approved by Utah State University's
Institutional Research Board.  In our Data Mining Group's folder:
USU - DM Research Project the Letter of Information of this research
from USU - Institutional Review Board.



I appreciate very much if you may forward this invitation to any
professional you may know that uses or plans to use Data Mining in any
area of a manufacturing organization.



Thank you very much for your help.





Jose D. Montero, Ph.D. Candidate

Work Tel. (435)863-3591

Home Tel. (801)776-5240

E-Mail: josedmontero@...
<mailto:josedmontero@...>

Folder: USU - DM Research Project



[Non-text portions of this message have been removed]

My company (a software company) is trying to build data mining algorithms OEMing
commercial tools (like SPSS or SAS) to do some predictions for all our
customers. the problem is that the we do not have any data (since our software
is still in development) and the algorithm has to work for all diverse
customers. Even when our product got implemented in the customer's enterprise
and start collecting data, we allow them to model their business process in such
dramatic different way.

the more typical process is that our customer should hire data analysts to
develop prediction algorithms based on the massive data. The problem I am facing
here is that we are trying to *automate* the tasks of data analysts and build a
generic enough model that fits all customers *without any data*.

For example, how can I determine a list of variables that will be having impact
on the prediction? These variables are usually discovered as patterns when
analysts start mining the data. These variables are also dependent on individual
enterprise and the presented data. Not to mention that at this time, we also
need to determine *what data* to collect.

I am so agonized over having to come up with a generic algorithm that'd fit all
these situations. What I figure is that any complex algorithms won't make sense.
The one make sense is probably as simple and universal as doing an AVG and take
that value to predict the future.

Any comments are highly appreciated.






[Non-text portions of this message have been removed]

My company (a software company) is trying to build data mining
algorithms OEMing commercial tools (like SPSS or SAS) to do some
predictions for all our customers. the problem is that the we do not
have any data (since our software is still in development) and the
algorithm has to work for all diverse customers.

Our software is a workflow tool that allows enterprises to model their
business processes; thus we have control over the platform/tool to
create the work flows, but our customers can create various work flows
that models their business problems

the more typical process is that our customer should hire data
analysts to develop prediction algorithms based on the massive data.
The problem I am facing here is that we are trying to *automate* the
tasks of data analysts and build a generic enough model that fits all
customers *without any data*. We are trying to create a model that
would predict the time to finish walking the work flow.

For example, how can I determine a list of variables that will be
having impact on the prediction? These variables are usually
discovered as patterns when analysts start mining the data. These
variables are also dependent on individual enterprise and the
presented data. Not to mention that at this time, we also need to
determine *what data* to collect.

I am so agonized over having to come up with a generic algorithm
that'd fit all these situations. What I figure is that any complex
algorithms won't make sense. The one make sense is probably as simple
and universal as doing an AVG and take that value to predict the future.

Any comments are highly appreciated.

I’m also  preparing a thesis about “Manufacturing  FlowtimeEstimation using
Data Mining ”.



The papers below can be useful  for your application.  You can search as
keywords like

flowtime estimation,

due date assignment etc.





Y.-F. Hung and C.-B. Chang, "Dispatching rules using flow time predictions for
semiconductor wafer fabrication," Journal of the Chinese Institute of Industrial
Engineers, vol. 19, no. 1, pp. 67 – 74, 2002.



I. Sabuncuoglu and A. Comlekci, "Operation-based flowtime estimation in a
dynamic job shop," Omega, vol. 30, pp. 423 - 442, 2002.



Planning production usingmathematical programming: The case of a woodturning
company Rafael Pastora,b,∗, Jordi Altimirasb, Manuel Mateob



“Manufacturing lead time estimation using data mining” Atakan Ozturk, Sinan
KayalÄągil, Nur E. Ozdemirel *



With best regards,



Gokhan Bilekdemir









From: datamining2@yahoogroups.com [mailto:datamining2@yahoogroups.com] On Behalf
Of whorl1quote1
Sent: Monday, February 09, 2009 8:07 PM
To: datamining2@yahoogroups.com
Subject: [Data Mining] How to develop data mining algorisms *without data*



My company (a software company) is trying to build data mining
algorithms OEMing commercial tools (like SPSS or SAS) to do some
predictions for all our customers. the problem is that the we do not
have any data (since our software is still in development) and the
algorithm has to work for all diverse customers.

Our software is a workflow tool that allows enterprises to model their
business processes; thus we have control over the platform/tool to
create the work flows, but our customers can create various work flows
that models their business problems

the more typical process is that our customer should hire data
analysts to develop prediction algorithms based on the massive data.
The problem I am facing here is that we are trying to *automate* the
tasks of data analysts and build a generic enough model that fits all
customers *without any data*. We are trying to create a model that
would predict the time to finish walking the work flow.

For example, how can I determine a list of variables that will be
having impact on the prediction? These variables are usually
discovered as patterns when analysts start mining the data. These
variables are also dependent on individual enterprise and the
presented data. Not to mention that at this time, we also need to
determine *what data* to collect.

I am so agonized over having to come up with a generic algorithm
that'd fit all these situations. What I figure is that any complex
algorithms won't make sense. The one make sense is probably as simple
and universal as doing an AVG and take that value to predict the future.

Any comments are highly appreciated.





[Non-text portions of this message have been removed]

Manager – Customer Marketing Analytics
Base $120k - $125k + 10-15% bonus
For Int'l Scientific Instrument Firm – northern Chicago land, IL
Must be US Citizen, PR, Green Card or TN Visa Holder
Relo allocation; drug screen/background ck conducted

Reports into Director of Direct Marketing
Annual sales of $10 billion; employ 33,000 people in 150 countries
worldwide, and serve over 350,000 customers.  There are 356 people in
the Division of this Company; 52 employed within the Marketing
Department.
Description
The Manager of Customer Marketing Analytics leads and provides
strategic direction to the direct marketing function of the Company
to identify customer analyses that will reveal critical customer
insights needed to develop B2B direct marketing programs to develop
response models, based on customers' behaviors; identifies and
prioritizes customer opportunities.
The successful candidate will also:
• Lead and manage a staff (5 direct reports).  This is a hands-on
position employing data mining techniques to identify appropriate
direct marketing contact strategies for their B2B catalog.  Build and
apply regression-based models for catalog mailing campaigns.
• Participate in establishing business objectives, developing
budgets, and providing summaries of program performance. Ensures a
coordinated global marketing program.
• Maintain awareness of key competitor developments and external best
practices, dynamically adjusting strategy as required.
• Develop processes to ensure critical business needs, to shorten
turnaround time on ad-hoc requests, and to provide deeper analytic
insights to researchers and cross business segment managers.
Requirements
Absolute must have's for this position, B2B within Direct Marketing,
strong and current SAS Programming, expert hands-on Data Mining
skills, strong management experience and strategic development.
• Minimum of 5 years of experience in marketing analytics including
customer modeling, managing specific segment development initiatives
including technical/analytic development process. Must have extensive
knowledge of statistical methods and system design used to analyze
customer data and behavior.
• Must have experience working directly with SAS and programming in
it and other data analytics tools.   (3 of the people that report
into this role are jr. level SAS programmers); Expert in Excel
• Must have B2B experience in a direct marketing environment.
• Strong management experience – 5 direct reports.  This is however a
hands-on lead job.
• Bachelor's degree required in Business Statistics or Marketing –
Graduate degree or MBA a plus
• Must have experience managing creative process to ensure high-
impact customer contacts in a catalog direct mail environment.  B2B
experience is a must!
• Must possess excellent communication, leadership, and
organizational skills to be able to integrate multiple business
needs, build compromise where necessary, and present ideas to senior
management.  Must be expert in developing a strategic plan.
• Must possess strong analytical skills and detail orientation to
interpret analytic findings into relevant and actionable business
insights and to recommend appropriate and effective strategies to
successfully assist in accomplishing business results. Company is
looking to establish methodology and put processes in place – driving
organization around these bylaws.
• Must have communication skills to encourage business to focus on
core customer issues and to produce a clear, concise strategic plan
to assist the organization in meeting its goals.
Position may require up to 10% travel – to occasional conferences

  Interested candidates e-mail resume and cover letter to:
Linda@...
Linda Benton, President
Benton Search Associates, Inc.
847.223.8804 www.BentonSearch.com

Dear Data Mining Group Member,


I need very much and appreciate your help in answering an online
survey at
http://www.zoomerang.com/Survey/?p=WEB228PJJH4JYT

This survey is part of a research that I am conducting on Analysis of
Applications of Data Mining to Manufacturing. This study is part of
my doctoral dissertation at the Department of Management Information
Systems of Utah State University on Analysis of Applications of Data
Mining to Manufacturing.

This survey aims to enhance the understanding on the factors that
affect the adoption of Data Mining by manufacturing organizations in
the USA.  The information gathered will be very valuable in
identifying barriers and drivers for the successful adoption of Data
Mining in all areas of manufacturing organizations.

This research has been approved by Utah State University's
Institutional Research Board.  In our Data Mining Group's folder:
USU - DM Research Project the Letter of Information of this research
from USU - Institutional Review Board.

I appreciate very much if you may also forward this invitation to any
professional you may know that uses or plans to use Data Mining in
any area of a manufacturing organization.

Thank you very much for your help.


Jose D. Montero, Ph.D. Candidate
Work Tel. (435)863-3591
Home Tel. (801)776-5240
E-Mail: josedmontero@...
Folder: USU - DM Research Project

Hi,
I am using ibm quest synthetic market basket data generator.
I have a problem about number of transactions. I give the parameter to
generate 10000 transactions but i have about 9850 transactions. The
only way for now seems to increase average number of items per
transaction to 20 but i want transactions with lower average items. I
wonder how can i come over this problem and if there is a detailed
information about this data generator and the parameters.
Thanks.

Baris

Hello,
 
Any one who involve in  research "Data Stream Mining in Wireless Sensor
Network"
 
I have some problems in this regards to discuss with him.
 
It is urgent plz.
 
Thanks,
 
Hassan, Mehdi


       Add more friends to your messenger and enjoy! Go to
http://messenger.yahoo.com/invite/

[Non-text portions of this message have been removed]

--- In datamining2@yahoogroups.com, mehdi hassan <mehdidgk@...>
wrote:
Dear Hassan
Good Wishes

       I am also doing project in "Data Mining for Wireless sensor
network" So What type of help you want, please feel free to asked
any query releted to this one.

Yours

B.K.Mishra

> Hello,
>  
> Any one who involve in  research "Data Stream Mining in Wireless
Sensor Network"
>  
> I have some problems in this regards to discuss with him.
>  
> It is urgent plz.
>  
> Thanks,
>  
> Hassan, Mehdi
>
>
>       Add more friends to your messenger and enjoy! Go to
http://messenger.yahoo.com/invite/
>
> [Non-text portions of this message have been removed]
>