Ajay Ohri

Interesting community event by R/Statistical community

Citation-
http://en.oreilly.com/oscon2009/public/schedule/detail/10432

StackOverflow Flash Mob for the R User Community
Moderated by: Michael E. Driscoll
7:00pm Wednesday, 07/22/2009
Location: Ballroom A2

In concert with users online across the country, this session will lead a flashmob to populate StackOverflow with R language content.

R, the open source statistical language, has a notoriously steep learning curve. The same technical questions tend be asked repeatedly on the R-help mailing lists, to the detriment of both R experts (who tire of repeating themselves) and the learners (who often receive a technically correct, but terse response).

We have developed a list of the most common 100 technical R questions, based on an analysis of (i) queries sent to the RSeek.org web portal, and (ii) an examination of the R-help list archives, and (iii) a survey of members of R Users Groups in San Francisco, LA, and New York City.

In the first hour, participants will pair up to claim a question, formulate it on StackOverflow, and provide a comprehensive answer. In the second hour, participants will rate, review, and comment on the set of submitted questions and answers.

While Stackoverflow currently lacks content for the R language, we believe this effort will provide the spark to attract more R users, and emerge as a valuable resource to the growing R community.

This is an interesting example of a statistical software community using twitter for a tech help event. I hope this trend/ event gets replicated again and again-

Statisticians worldwide unite in the language of maths !!!

Please follow @rstatsmob to participate. See you at 7 PM PST!

twitter.com/Rstatsmob

Growing Rapidly: Rapid Miner 4.5

The Europe based Rapid Miner came out with version 4.5 of their data mining tool ( also known as Yale) with a much promising “Script” tool.

Also, Rapid Miner came in 1st in open source data mining tools in a poll by Industry benchmark www.kdnuggets.com

They have a brilliant video here for people who just want to have a look at the new Rapid Miner

http://rapid-i.com/videos/rapidminer_tour_3_4_en.html

Citation-

http://rapid-i.com/content/view/147/1/

New Operators:

FormulaExtractor
Trend
LagSeries
VectorLinearRegression
ExampleSetMinus
ExampleSetIntersect
Partition
Script
ForwardSelection
NeuralNetImproved
KernelNaiveBayes
ExhaustiveSubgroupDiscovery
URLExampleSource
NonDominatedSorting

More Features:

The new Script operator allows for arbitrary user defined operations based on Groovy script combined with a simplified RapidMiner syntax
Improved the join operator and added options for left and right outer joins
New notification mail mechanism at the end of processes
Most file based data input operators now provide an option to skip error lines
Most file based example source operators as well as the IOObjectReader and the new URLExampleSource now accept URLs instead of a filename for the input source location

Managing Twitter Relationships:Refollow.com

If you have more than 100 followers, or people following you on Twitter and want to have some kind of Outlook like manager for managing so much info- this is a great tool from www.refollow.com

Added benefits-
1) Secure Login using Twitter Authorization
2) Visual Click and Easy Follow- Unfollow Blocking based on activities
3) Segmenting groups of people basede on behavior
4) Hidden insights on who all suddenly have un-followed me ( after I accidentally revealed the spoiler end of latest Harry Potter movie- Dumbeldore will sleep with the fishes)

The Screenshot (of my refollow page) below shows you most of the properties-

R language on the GPU

Here are some nice articles on using R on Graphical Processing Units (GPU) mainly made by NVidia. Think of a GPU as a customized desktop with specialized computing equivalent to much faster computing. i.e. Matlab users can read the webinars here http://www.nvidia.com/object/webinar.html

Now a slightly better definition of GPU computing is from http://www.nvidia.com/object/GPU_Computing.html

GPU computing is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing.
The model for GPU computing is to use a CPU and GPU together in a heterogeneous computing model. The sequential part of the application runs on the CPU and the computationally-intensive part runs on the GPU. From the user’s perspective, the application just runs faster because it is using the high-performance of the GPU to boost performance.

rgpu

Citation:

http://brainarray.mbni.med.umich.edu/brainarray/rgpgpu/

R is the most popular open source statistical environment in the biomedical research community. However, most of the popular R function implementations involve no parallelism and they can only be executed as separate instances on multicore or cluster hardware for large data-parallel analysis tasks. The arrival of modern graphics processing units (GPUs) with user friendly programming tools, such as nVidia’s CUDA toolkit (http://www.nvidia.com/cuda), provides a possibility of increasing the computational efficiency of many common tasks by more than one order of magnitude (http://gpgpu.org/). However, most R users are not trained to program a GPU, a key obstacle for the widespread adoption of GPUs in biomedical research.

The research project at the page mentioned above has developed special packages for the above need- R on a GPU.

he initial package is hosted by CRAN as gputools a sorce package for UNIX and Linux systems. Be sure to set the environment variable CUDA_HOME to the root of your CUDA toolkit installation. Then install the package in the usual R manner. The installation process will automatically make use of nVidia’s nvcc compiler and CUBLAS shared library.

and some figures

Figure 1 provides performance comparisons between original R functions assuming a four thread data parallel solution on Intel Core i7 920 and our GPU enabled R functions for a GTX 295 GPU. The speedup test consisted of testing each of three algorithms with five randomly generated data sets. The Granger causality algorithm was tested with a lag of 2 for 200, 400, 600, 800, and 1000 random variables with 10 observations each. Complete hierarchical clustering was tested with 1000, 2000, 4000, 6000, and 8000 points. Calculation of Kendall’s correlation coefficient was tested with 20, 30, 40, 50, and 60 random variables with 10000 observations each

Ajay- For hard core data mining people ,customized GPU’s for accelerated analytics and data mining sounds like fun and common sense. Are there other packages for customization on a GPU – let me know.

Citation:

http://brainarray.mbni.med.umich.edu/brainarray/rgpgpu/