Computer Education grants from Google

Image representing Google as depicted in Crunc...
Image via CrunchBase

message from the official google blog-

http://googleblog.blogspot.com/2011/01/supporting-computer-science-education.html

With programs like Computer Science for High School (CS4HS), we hope to increase the number of CS majors —and therefore the number of people entering into careers in CS—by promoting computer science curriculum at the high school level.

For the fourth consecutive year, we’re funding CS4HS to invest in the next generation of computer scientists and engineers. CS4HS is a workshop for high school and middle school computer science teachers that introduces new and emerging concepts in computing and provides tips, tools and guidance on how to teach them. The ultimate goals are to “train the trainer,” develop a thriving community of high school CS teachers and spread the word about the awe and beauty of computing.

If you’re a university, community college, or technical School in the U.S., Canada, Europe, Middle East or Africa and are interested in hosting a workshop at your institution, please visit www.cs4hs.com to submit an application for grant funding.Applications will be accepted between January 18, 2011 and February 18, 2011.

In addition to submitting your application, on the CS4HS website you’ll find info on how to organize a workshop, as well as websites and agendas from last year’s participants to give you an idea of how the workshops were structured in the past. There’s also a collection ofCS4HS curriculum modules that previous participating schools have shared for future organizers to use in their own program.

Using R from within Python

Python logo
Image via Wikipedia

I came across this excellent JSS paper at www.jstatsoft.org/v35/c02/paper

on a Python package called PypeR which allows you to use R from within Python using the pipe functionality.

It is an interesting package and given Python’s increasing buzz , one worthy to be checked out by people using or thinking Python in their packages.

























Citation:
	@article{Xia:McClelland:Wang:2010:JSSOBK:v35c02,
	  author =	"Xiao-Qin Xia and Michael McClelland and Yipeng Wang",
	  title =	"PypeR, A Python Package for Using R in Python",
	  journal =	"Journal of Statistical Software, Code Snippets",
	  volume =	"35",
	  number =	"2",
	  pages =	"1--8",
	  day =  	"30",
	  month =	"7",
	  year = 	"2010",
	  CODEN =	"JSSOBK",
	  ISSN = 	"1548-7660",
	  bibdate =	"2010-03-23",
	  URL =  	"http://www.jstatsoft.org/v35/c02",
	  accepted =	"2010-03-23",
	  acknowledgement = "",
	  keywords =	"",
	  submitted =	"2009-10-23",
	}

 

PySpread Magic

Python logo
Image via Wikipedia

Just working with PySpread- and worked on a 1 million by 1 million spreadsheet- Python sure looks promising for the way ahead for stat computing ( you need to

sudo apt-get install python-numpy python-rpy python-scipy python-gmpy wxpython*,

cd to the untarred bz2 file from http://pyspread.sourceforge.net/download.html,  (like

:~/Downloads$ cd pyspread-0.1.2

:~/Downloads/pyspread-0.1.2

sudo python setup.py install

)

http://pyspread.sourceforge.net/

by Martin Manns

 

about Pyspread is a cross-platform Python spreadsheet application. It is based on and written in the programming language Python.

Instead of spreadsheet formulas, Python expressions are entered into the spreadsheet cells. Each expression returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices.

Pyspread screenshot
features
  • Three dimensional grid with up to 85,899,345 rows and 14,316,555 columns (64 bit systems, depends on row height and column width). Note that a million cells require about 500 MB of memory.
  • Complex data types such as lists, trees or matrices within a single cell.
  • Macros for functionalities that are too complex for a single Python expression.
  • Python module access from each cell, which allows:
    • Arbitrary size rational numbers (via gmpy),
    • Fixed point decimal numbers for business calculations, (via the decimal module from the standard library)
    • Advanced statistics including plotting functions (via RPy)
    • Much more via <your favourite module>.
  • CSV import and export
  • Clipboard access
Pyspread screenshot

warning The concept of pyspread allows doing everything from each cell that a Python script can do. This powerful feature has its drawbacks. A spreadsheet may very well delete your hard drive or send your data via the Internet. Of course this is a non-issue if you sandbox properly or if you only use self developed spreadsheets.

Since this is not the case for everyone (see discussion at lwn.net), a GPG signature based trust model for spreadsheet files has been introduced. It ensures that only your own trusted files are executed on loading. Untrusted files are displayed in safe mode. You can approve a file manually. Inspect carefully.

 

Cloud Computing with R

Illusion of Depth and Space (4/22) - Rotating ...
Image by Dominic's pics via Flickr

Here is a short list of resources and material I put together as starting points for R and Cloud Computing It’s a bit messy but overall should serve quite comprehensively.

Cloud computing is a commonly used expression to imply a generational change in computing from desktop-servers to remote and massive computing connections,shared computers, enabled by high bandwidth across the internet.

As per the National Institute of Standards and Technology Definition,
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

(Citation: The NIST Definition of Cloud Computing

Authors: Peter Mell and Tim Grance
Version 15, 10-7-09
National Institute of Standards and Technology, Information Technology Laboratory
http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc)

R is an integrated suite of software facilities for data manipulation, calculation and graphical display.

From http://cran.r-project.org/doc/FAQ/R-FAQ.html#R-Web-Interfaces

R Web Interfaces

Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page provides access to all three versions of Rweb—a simple text entry form that returns output and graphs, a more sophisticated JavaScript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language. All of the Rweb versions can analyze Web accessible datasets if a URL is provided.
The paper “Rweb: Web-based Statistical Analysis”, providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.jstatsoft.org/v04/i01/).

Ulf Bartel has developed R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible. There is no need for a local installation since the only requirement for the user is a JavaScript capable browser. See http://osvisions.com/r-online/ for more information.

Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix user input and code, allowing the HTMLauthor to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active.

Currently, a modified version of Rcgi by Mai Zhou (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from http://www.ms.uky.edu/~statweb/.

CGI-based web access to R is also provided at http://hermes.sdu.dk/cgi-bin/go/. There are many additional examples of web interfaces to R which basically allow to submit R code to a remote server, see for example the collection of links available from http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse.

David Firth has written CGIwithR, an R add-on package available from CRAN. It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server, and allows submission of data using both GET and POST methods. It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions. David’s paper “CGIwithR: Facilities for Processing Web Forms Using R” was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/). The package is now maintained by Duncan Temple Lang and has a web page athttp://www.omegahat.org/CGIwithR/.

Rpad, developed and actively maintained by Tom Short, provides a sophisticated environment which combines some of the features of the previous approaches with quite a bit of JavaScript, allowing for a GUI-like behavior (with sortable tables, clickable graphics, editable output), etc.
Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond). A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.

Rserve is a project actively developed by Simon Urbanek. It implements a TCP/IP server which allows other programs to use facilities of R. Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets).

OpenStatServer is being developed by a team lead by Greg Warnes; it aims “to provide clean access to computational modules defined in a variety of computational environments (R, SAS, Matlab, etc) via a single well-defined client interface” and to turn computational services into web services.

Two projects use PHP to provide a web interface to R. R_PHP_Online by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input.

webbioc is “an integrated web interface for doing microarray analysis using several of the Bioconductor packages” and is designed to be installed at local sites as a shared computing resource.

Rwui is a web application to create user-friendly web interfaces for R scripts. All code for the web interface is created automatically. There is no need for the user to do any extra scripting or learn any new scripting techniques. Rwui can also be found at http://rwui.cryst.bbk.ac.uk.

Finally, the R.rsp package by Henrik Bengtsson introduces “R Server Pages”. Analogous to Java Server Pages, an R server page is typically HTMLwith embedded R code that gets evaluated when the page is requested. The package includes an internal cross-platform HTTP server implemented in Tcl, so provides a good framework for including web-based user interfaces in packages. The approach is similar to the use of the brew package withRapache with the advantage of cross-platform support and easy installation.

Also additional R Cloud Computing Use Cases
http://wwwdev.ebi.ac.uk/Tools/rcloud/

ArrayExpress R/Bioconductor Workbench

Remote access to R/Bioconductor on EBI’s 64-bit Linux Cluster

Start the workbench by downloading the package for your operating system (Macintosh or Windows), or via Java Web Start, and you will get access to an instance of R running on one of EBI’s powerful machines. You can install additional packages, upload your own data, work with graphics and collaborate with colleagues, all as if you are running R locally, but unlimited by your machine’s memory, processor or data storage capacity.

  • Most up-to-date R version built for multicore CPUs
  • Access to all Bioconductor packages
  • Access to our computing infrastructure
  • Fast access to data stored in EBI’s repositories (e.g., public microarray data in ArrayExpress)

Using R Google Docs
http://www.omegahat.org/RGoogleDocs/run.pdf
It uses the XML and RCurl packages and illustrates that it is relatively quick and easy
to use their primitives to interact with Web services.

Using R with Amazon
Citation
http://rgrossman.com/2009/05/17/running-r-on-amazons-ec2/

Amazon’s EC2 is a type of cloud that provides on demand computing infrastructures called an Amazon Machine Images or AMIs. In general, these types of cloud provide several benefits:

  • Simple and convenient to use. An AMI contains your applications, libraries, data and all associated configuration settings. You simply access it. You don’t need to configure it. This applies not only to applications like R, but also can include any third-party data that you require.
  • On-demand availability. AMIs are available over the Internet whenever you need them. You can configure the AMIs yourself without involving the service provider. You don’t need to order any hardware and set it up.
  • Elastic access. With elastic access, you can rapidly provision and access the additional resources you need. Again, no human intervention from the service provider is required. This type of elastic capacity can be used to handle surge requirements when you might need many machines for a short time in order to complete a computation.
  • Pay per use. The cost of 1 AMI for 100 hours and 100 AMI for 1 hour is the same. With pay per use pricing, which is sometimes called utility pricing, you simply pay for the resources that you use.

Connecting to R on Amazon EC2- Detailed tutorials
Ubuntu Linux version
https://decisionstats.com/2010/09/25/running-r-on-amazon-ec2/
and Windows R version
https://decisionstats.com/2010/10/02/running-r-on-amazon-ec2-windows/

Connecting R to Data on Google Storage and Computing on Google Prediction API
https://github.com/onertipaday/predictionapirwrapper
R wrapper for working with Google Prediction API

This package consists in a bunch of functions allowing the user to test Google Prediction API from R.
It requires the user to have access to both Google Storage for Developers and Google Prediction API:
see
http://code.google.com/apis/storage/ and http://code.google.com/apis/predict/ for details.

Example usage:

#This example requires you had previously created a bucket named data_language on your Google Storage and you had uploaded a CSV file named language_id.txt (your data) into this bucket – see for details
library(predictionapirwrapper)

and Elastic R for Cloud Computing
http://user2010.org/tutorials/Chine.html

Abstract

Elastic-R is a new portal built using the Biocep-R platform. It enables statisticians, computational scientists, financial analysts, educators and students to use cloud resources seamlessly; to work with R engines and use their full capabilities from within simple browsers; to collaborate, share and reuse functions, algorithms, user interfaces, R sessions, servers; and to perform elastic distributed computing with any number of virtual machines to solve computationally intensive problems.
Also see Karim Chine’s http://biocep-distrib.r-forge.r-project.org/

R for Salesforce.com

At the point of writing this, there seem to be zero R based apps on Salesforce.com This could be a big opportunity for developers as both Apex and R have similar structures Developers could write free code in R and charge for their translated version in Apex on Salesforce.com

Force.com and Salesforce have many (1009) apps at
http://sites.force.com/appexchange/home for cloud computing for
businesses, but very few forecasting and statistical simulation apps.

Example of Monte Carlo based app is here
http://sites.force.com/appexchange/listingDetail?listingId=a0N300000016cT9EAI#

These are like iPhone apps except meant for business purposes (I am
unaware if any university is offering salesforce.com integration
though google apps and amazon related research seems to be on)

Force.com uses a language called Apex  and you can see
http://wiki.developerforce.com/index.php/App_Logic and
http://wiki.developerforce.com/index.php/An_Introduction_to_Formulas
Apex is similar to R in that is OOPs

SAS Institute has an existing product for taking in Salesforce.com data.

A new SAS data surveyor is
available to access data from the Customer Relationship Management
(CRM) software vendor Salesforce.com. at
http://support.sas.com/documentation/cdl/en/whatsnew/62580/HTML/default/viewer.htm#datasurveyorwhatsnew902.htm)

Personal Note-Mentioning SAS in an email to a R list is a big no-no in terms of getting a response and love. Same for being careless about which R help list to email (like R devel or R packages or R help)

For python based cloud see http://pi-cloud.com

Here comes PySpread- 85,899,345 rows and 14,316,555 columns

A Bold GNU Head
Image via Wikipedia

Whats new/ One more open source analytics package. Built like a spreadsheet with an ability to import a million cells-

From http://pyspread.sourceforge.net/index.html

about Pyspread is a cross-platform Python spreadsheet application. It is based on and written in the programming language Python.

Instead of spreadsheet formulas, Python expressions are entered into the spreadsheet cells. Each expression returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices.

Pyspread screenshot
features In pyspread, cells expect Python expressions and return Python objects. Therefore, complex data types such as lists, trees or matrices can be handled within a single cell. Macros can be used for functions that are too complex for a single expression.

Since Python modules can be easily used without external scripts, arbitrary size rational numbers (via gmpy), fixed point decimal numbers for business calculations, (via the decimal module from the standard library) and advanced statistics including plotting functions (via RPy) can be used in the spreadsheet. Everything is directly available from each cell. Just use the grid

Data can be imported and exported using csv files or the clipboard. Other forms of data exchange is possible using external Python modules.

In  order to simplify sparse matrix editing, pyspread features a three dimensional grid that can be sized up to 85,899,345 rows and 14,316,555 columns (64 bit-systems, depends on row height and column width). Note that importing a million cells requires about 500 MB of memory.

The concept of pyspread allows doing everything from each cell that a Python script can do. This may very well include deleting your hard drive or sending your data via the Internet. Of course this is a non-issue if you sandbox properly or if you only use self developed spreadsheets. Since this is not the case for everyone (see the discussion at lwn.net), a GPG signature based trust model for spreadsheet files has been introduced. It ensures that only your own trusted files are executed on loading. Untrusted files are displayed in safe mode. You can trust a file manually. Inspect carefully.

Pyspread screenshot

requirements Pyspread runs on Linux, Windows and *nix platforms with GTK+ support. There are reports that it works with MacOS X as well. If you would like to contribute by testing on OS X please contact me.

Dependencies

Highly recommended for full functionality

  • PyMe >=0.8.1, Note for Windows™ users: If you want to use signatures without compiling PyMe try out Gpg4win.
  • gmpy >=1.1.0 and
  • rpy >=1.0.3.
maturity Pyspread is in early Beta release. This means that the core functionality is fully implemented but the program needs testing and polish.

and from the wiki

http://sourceforge.net/apps/mediawiki/pyspread/index.php?title=Main_Page

a spreadsheet with more powerful functions and data structures that are accessible inside each cell. Something like Python that empowers you to do things quickly. And yes, it should be free and it should run on Linux as well as on Windows. I looked around and found nothing that suited me. Therefore, I started pyspread.

Concept

  • Each cell accepts any input that works in a Python command line.
  • The inputs are parsed and evaluated by Python’s eval command.
  • The result objects are accessible via a 3D numpy object array.
  • String representations of the result objects are displayed in the cells.

Benefits

  • Each cell returns a Python object. This object can be anything including arrays and third party library objects.
  • Generator expressions can be used efficiently for data manipulation.
  • Efficient numpy slicing is used.
  • numpy methods are accessible for the data.

Installation

  1. Download the pyspread tarball or zip and unzip at a convenient place
  2. In case you do not have it already get and install Python, wxpython and numpy
If you want the examples to work, install gmpy, R and rpy
Really do check the version requirements that are mentioned on http://pyspread.sf.net
  1. Get install privileges (e.g. become root)
  2. Change into the directory and type
python setup.py install
Windows: Replace “python” with your Python interpreter (absolute path)
  1. Become normal user again
  2. Start pyspread by typing
pyspread
  1. Enjoy

Links

Next on Spreadsheet wishlist-

a MSI bundle /Windows Self Installer which has all dependencies bundled in it-linking to PostGresSQL 😉 etc

way to go Mr Martin Manns

mmanns < at > gmx < dot > net

Going Deap : Algols in Python

Logo of PyPy
Image via Wikipedia

Here is an important new step in Python- the established statistical programming language (used to be really pushed by SPSS in pre-IBM days and the rPy package integrates R and Python).

Well the news  ( http://www.kdnuggets.com/2010/10/eap-evolutionary-algorithms-in-python.html ) is the release of Distributed Evolutionary Algorithms in Python. If your understanding of modeling means running regression and iterating it- you may need to read some more.  If you have felt frustrated at lack of parallelization in statistical software as well as your own hardware constraints- well go DEAP (and for corporate types the licensing is

http://www.gnu.org/licenses/lgpl.html ).

http://code.google.com/p/deap/

DEAP

DEAP is intended to be an easy to use distributed evolutionary algorithm library in the Python language. Its two main components are modular and can be used separately. The first module is a Distributed Task Manager (DTM), which is intended to run on cluster of computers. The second part is the Evolutionary Algorithms in Python (EAP) framework.

DTM

DTM is a distributed task manager that is able to spread workload over a buch of computers using a TCP or a MPI connection.

DTM include the following features:

 

EAP

Features

EAP includes the following features:

  • Genetic algorithm using any imaginable representation
    • List, Array, Set, Dictionary, Tree, …
  • Genetic programing using prefix trees
    • Loosely typed, Strongly typed
    • Automatically defined functions (new v0.6)
  • Evolution strategies (including CMA-ES)
  • Multi-objective optimisation (NSGA-II, SPEA-II)
  • Parallelization of the evaluations (and maybe more) (requires python2.6 and preferably python2.7) (new v0.6)
  • Genealogy of an evolution (that is compatible with NetworkX) (new v0.6)
  • Hall of Fame of the best individuals that lived in the population (new v0.5)
  • Milestones that take snapshot of a system regularly (new v0.5)

 

Documentation

See the eap user’s guide for EAP 0.6 documentation.

Requirement

The most basic features of EAP requires Python2.5 (we simply do not offer support for 2.4). In order to use multiprocessing you will need Python2.6 and to be able to combine the toolbox and the multiprocessing module Python2.7 is needed for its support to pickle partial functions.

Projects using EAP

If you want your project listed here, simply send us a link and a brief description and we’ll be glad to add it.

and from the wordpress.com blog (funny how people like code.google.com but not blogger.google.com anymore) at http://deapdev.wordpress.com/

EAP is part of the DEAP project, that also includes some facilities for the automatic distribution and parallelization of tasks over a cluster of computers. The D part of DEAP, called DTM, is under intense development and currently available as an alpha version. DTM currently provides two and a half ways to distribute workload on a cluster or LAN of workstations, based on MPI and TCP communication managers.

This public release (version 0.6) is more complete and simpler than ever. It includes Genetic Algorithms using any imaginable representation, Genetic Programming with strongly and loosely typed trees in addition to automatically defined functions, Evolution Strategies (including Covariance Matrix Adaptation), multiobjective optimization techniques (NSGA-II and SPEA2), easy parallelization of algorithms and much more like milestones, genealogy, etc.

We are impatient to hear your feedback and comments on that system at .

Best,

François-Michel De Rainville
Félix-Antoine Fortin
Marc-André Gardner
Christian Gagné
Marc Parizeau

Laboratoire de vision et systèmes numériques
Département de génie électrique et génie informatique
Université Laval
Quebec City (Quebec), Canada

and if you are new to Python -sigh here are some statistical things (read ad-van-cED analytics using Python) by a slideshare from Visual numerics (pre Rogue Wave acquisition)

Also see,

http://code.google.com/p/deap/wiki/SimpleExample

 

 

 

Interview Michael J. A. Berry Data Miners, Inc

Here is an interview with noted Data Mining practitioner Michael Berry, author of seminal books in data mining, noted trainer and consultantmjab picture

Ajay- Your famous book “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management” came out in 2004, and an update is being planned for 2011. What are the various new data mining techniques and their application that you intend to talk about in that book.

Michael- Each time we do a revision, it feels like writing a whole new book. The first edition came out in 1997 and it is hard to believe how much the world has changed since then. I’m currently spending most of my time in the on-line retailing world. The things I worry about today–improving recommendations for cross-sell and up-sell,and search engine optimization–wouldn’t have even made sense to me back then. And the data sizes that are routine today were beyond the capacity of the most powerful super computers of the nineties. But, if possible, Gordon and I have changed even more than the data mining landscape. What has changed us is experience. We learned an awful lot between the first and second editions, and I think we’ve learned even more between the second and third.

One consequence is that we now have to discipline ourselves to avoid making the book too heavy to lift. For the first edition, we could write everything we knew (and arguably, a bit more!); now we have to remind ourselves that our intended audience is still the same–intelligent laymen with a practical interest in getting more information out of data. Not statisticians. Not computer scientists. Not academic researchers. Although we welcome all readers, we are primarily writing for someone who works in a marketing department and has a title with the word “analyst” or “analytics” in it. We have relaxed our “no equations” rule slightly for cases when the equations really do make things easier to explain, but the core explanations are still in words and pictures.

The third edition completes a transition that was already happening in the second edition. We have fully embraced standard statistical modeling techniques as full-fledged components of the data miner’s toolkit. In the first edition, it seemed important to make a distinction between old, dull, statistics, and new, cool, data mining. By the second edition, we realized that didn’t really make sense, but remnants of that attitude persisted. The third edition rectifies this. There is a chapter on statistical modeling techniques that explains linear and logistic regression, naive Bayes models, and more. There is also a brand new chapter on text mining, a curious omission from previous editions.

There is also a lot more material on data preparation. Three whole chapters are devoted to various aspects of data preparation. The first focuses on creating customer signatures. The second is focused on using derived variables to bring information to the surface, and the third deals with data reduction techniques such as principal components. Since this is where we spend the greatest part of our time in our work, it seemed important to spend more time on these subjects in the book as well.

Some of the chapters have been beefed up a bit. The neural network chapter now includes radial basis functions in addition to multi-layer perceptrons. The clustering chapter has been split into two chapters to accommodate new material on soft clustering, self-organizing maps, and more. The survival analysis chapter is much improved and includes material on some of our recent application of survival analysis methods to forecasting. The genetic algorithms chapter now includes a discussion of swarm intelligence.

Ajay- Describe your early career and how you came into Data Mining as a profession. What do you think of various universities now offering MS in Analytics. How do you balance your own teaching experience with your consulting projects at The Data Miners.

Michael- I fell into data mining quite by accident. I guess I always had a latent interest in the topic. As a high school and college student, I was a fan of Martin Gardner‘s mathematical games in in Scientific American. One of my favorite things he wrote about was a game called New Eleusis in which one players, God, makes up a rule to govern how cards can be played (“an even card must be followed by a red card”, say) and the other players have to figure out the rule by watching what plays are allowed by God and which ones are rejected. Just for my own amusement, I wrote a computer program to play the game and presented it at the IJCAI conference in, I think, 1981.

That paper became a chapter in a book on computer game playing–so my first book was about finding patterns in data. Aside from that, my interest in finding patterns in data lay dormant for years. At Thinking Machines, I was in the compiler group. In particular, I was responsible for the run-time system of the first Fortran Compiler for the CM-2 and I represented Thinking Machines at the Fortran 8X (later Fortran-90) standards meetings.

What changed my direction was that Thinking Machines got an export license to sell our first machine overseas. The machine went to a research lab just outside of Paris. The connection machine was so hard to program, that if you bought one, you got an applications engineer to go along with it. None of the applications engineers wanted to go live in Paris for a few months, but I did.

Paris was a lot of fun, and so, I discovered, was actually working on applications. When I came back to the states, I stuck with that applied focus and my next assignment was to spend a couple of years at Epsilon, (then a subsidiary of American Express) working on a database marketing system that stored all the “records of charge” for American Express card members. The purpose of the system was to pick ads to go in the billing envelope. I also worked on some more general purpose data mining software for the CM-5.

When Thinking Machines folded, I had the opportunity to open a Cambridge office for a Virginia-based consulting company called MRJ that had been a major channel for placing Connection Machines in various government agencies. The new group at MRJ was focused on data mining applications in the commercial market. At least, that was the idea. It turned out that they were more interested in data warehousing projects, so after a while we parted company.

That led to the formation of Data Miners. My two partners in Data Miners, Gordon Linoff and Brij Masand, share the Thinking Machines background.

To tell the truth, I really don’t know much about the university programs in data mining that have started to crop up. I’ve visited the one at NC State, but not any of the others.

I myself teach a class in “Marketing Analytics” at the Carroll School of Management at Boston College. It is an elective part of the MBA program there. I also teach short classes for corporations on their sites and at various conferences.

Ajay- At the previous Predictive Analytics World, you took a session on Forecasting and Predicting Subsciber levels (http://www.predictiveanalyticsworld.com/dc/2009/agenda.php#day2-6) .

It seems inability to forecast is a problem many many companies face today. What do you think are the top 5 principles of business forecasting which companies need to follow.

Michael- I don’t think I can come up with five. Our approach to forecasting is essentially simulation. We try to model the underlying processes and then turn the crank to see what happens. If there is a principal behind that, I guess it is to approach a forecast from the bottom up rather than treating aggregate numbers as a time series.

Ajay- You often partner your talks with SAS Institute, and your blog at http://blog.data-miners.com/ sometimes contain SAS code as well. What particular features of the SAS software do you like. Do you use just the Enterprise Miner or other modules as well for Survival Analysis or Forecasting.

Michael- Our first data mining class used SGI’s Mineset for the hands-on examples. Later we developed versions using Clementine, Quadstone, and SAS Enterprise Miner. Then, market forces took hold. We don’t market our classes ourselves, we depend on others to market them and then share in the revenue.

SAS turned out to be much better at marketing our classes than the other companies, so over time we stopped updating the other versions. An odd thing about our relationship with SAS is that it is only with the education group. They let us use Enterprise Miner to develop course materials, but we are explicitly forbidden to use it in our consulting work. As a consequence, we don’t use it much outside of the classroom.

Ajay- Also any other software you use (apart from SQL and J)

Michael- We try to fit in with whatever environment our client has set up. That almost always is SQL-based (Teradata, Oracle, SQL Server, . . .). Often SAS Stat is also available and sometimes Enterprise Miner.

We run into SPSS, Statistica, Angoss, and other tools as well. We tend to work in big data environments so we’ve also had occasion to use Ab Initio and, more recently, Hadoop. I expect to be seeing more of that.

Biography-

Together with his colleague, Gordon Linoff, Michael Berry is author of some of the most widely read and respected books on data mining. These best sellers in the field have been translated into many languages. Michael is an active practitioner of data mining. His books reflect many years of practical, hands-on experience down in the data mines.

Data Mining Techniques cover

Data Mining Techniques for Marketing, Sales and Customer Relationship Management

by Michael J. A. Berry and Gordon S. Linoff
copyright 2004 by John Wiley & Sons
ISB

Mining the Web cover

Mining the Web

by Michael J.A. Berry and Gordon S. Linoff
copyright 2002 by John Wiley & Sons
ISBN 0-471-41609-6

Non-English editions available in Traditional Chinese and Simplified Chinese

This book looks at the new opportunities and challenges for data mining that have been created by the web. The book demonstrates how to apply data mining to specific types of online businesses, such as auction sites, B2B trading exchanges, click-and-mortar retailers, subscription sites, and online retailers of digital content.

Mastering Data Mining

by Michael J.A. Berry and Gordon S. Linoff
copyright 2000 by John Wiley & Sons
ISBN 0-471-33123-6

Non-English editions available in JapaneseItalianTraditional Chinese , and Simplified Chinese

A case study-based guide to applying data mining techniques for solving practical business problems. These “warts and all” case studies are drawn directly from consulting engagements performed by the authors.

A data mining educator as well as a consultant, Michael is in demand as a keynote speaker and seminar leader in the area of data mining generally and the application of data mining to customer relationship management in particular.

Prior to founding Data Miners in December, 1997, Michael spent 8 years at Thinking Machines Corporation. There he specialized in the application of massively parallel supercomputing techniques to business and marketing applications, including one of the largest database marketing systems of the time.