R Data Mining Resources

Data Mining using R has been gaining popularity among data miner/data analyst around the globe these days. A report from the Rexer’s Annual Data Miner Survey in 2010 stated that R has become the data mining tool used by more data miners (43%). According to Wikipedia, R is a programming language and software environment for statistical computing and graphics. R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. Thus, I have compiled top resources about R data mining for your reference:

  1. R Project for Statistical Computing – the official R open source project website. Here you can get the latest release of R source code, manuals and recent bugs.
  2. R Books Website – list of latest books that are related to R and may be useful to the R user community. You may also like to read Data Mining with R book (data mining bestseller at Amazon.com).
  3. R in Wikipedia – here you can read basic info and example for R programming, including list of GUI for R and some references.
  4. Rattle: A GUI for Data Mining using R – a simple and logical graphical user interface based on Gnome that can be used by itself to deliver data mining projects. Rattle runs under GNU/Linux, Macintosh OS/X, and MS/Windows.
  5. R reference card for data mining – a collection of R packages and functions for data mining.
  6. R Bloggers – a central hub of news and tutorials contributed by (185) R bloggers.
  7. R Video Tutorials – a series of R for Statistical Programming screencasts that show you how to use R for for text mining. (some of the video links are missing)
  8. Reasons to learn R? – YouTube video describing why students should learn the R programming language.
  9. Programming R – online R programming resources from beginner to advanced resources.
  10. R Programming Wikibook – a place where anyone can share his/her tricks and knowledge on R.

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Algorithm News

Choosing an algorithm – benchmarking bioinformatics – Sciblogs (blog)

Choosing an algorithm – benchmarking bioinformatics
Sciblogs (blog)
How do you choose what algorithm to use? My first suggestion would be to talk to experienced bioinformatics scientists or computational biologists. …
Algorithm method ‘reliably performs’ quantitative analysis of electrophoresis … – Chromatography Today

Algorithm method ‘reliably performs’ quantitative analysis of electrophoresis …
Chromatography Today
A computational algorithm devised by a team in Taiwan is able to simplify the quantitative analysis of results obtained by electrophoresis, they claim. …
Exagen Diagnostics Announces Purchase of Cypress Diagnostic Business – MarketWatch (press release)

Exagen Diagnostics Announces Purchase of Cypress Diagnostic Business
MarketWatch (press release)
Using Coperna(R), the company’s proprietary, algorithm-driven computational tool and search engine that runs on high-performance computational clusters, …
Exagen Picks Up Cypress’ Diagnostic Business for $4MGenetic Engineering News
all 19 news articles »
Cypress Bioscience Announces Agreement to Sell Diagnostic Business to Exagen … – MarketWatch (press release)

Cypress Bioscience Announces Agreement to Sell Diagnostic Business to Exagen …
MarketWatch (press release)
Using Coperna(R), the company’s proprietary, algorithm-driven computational tool and search engine that runs on high performance computational clusters, …
and more »
Auxogyn Licenses Non-invasive Embryo Assessment Technology From Stanford … – PR Newswire (press release)

Auxogyn Licenses Non-invasive Embryo Assessment Technology From Stanford …
PR Newswire (press release)
Auxogyn’s first product in development combines a proprietary computational algorithm with developmental biology imaging capabilities to assess early embryo …
and more »
US researchers create peptide algorithm for LC-MS – Chromatography Today

US researchers create peptide algorithm for LC-MS
Chromatography Today
… inferred and observed spectrum in each case. BMC Bioinformatics is concerned with the latest statistical and computational methods for scientific analysis.
Computational analysis of LexA regulons in Cyanobacteria – 7thSpace Interactive (press release)

Computational analysis of LexA regulons in Cyanobacteria
7thSpace Interactive (press release)
… we have predicted their LexA-binding sites and regulons using an efficient binding site/regulon prediction algorithm that we developed previously. …
Call to be generous with our forgiveness – Network Norwich

Network Norwich

Call to be generous with our forgiveness
Network Norwich
And given what I just said about computation and causality being beyond the scope of our knowledge, I believe that the scientific models that zoom in on …
and more »
A global optimization algorithm for protein surface alignment – 7thSpace Interactive (press release)

A global optimization algorithm for protein surface alignment
7thSpace Interactive (press release)
The reported computational experience and comparison show viability of the proposed approach. Conclusions: Our method performs well to detect similarity in …
Inferring the conservative causal core of gene regulatory networks – 7thSpace Interactive (press release)

Inferring the conservative causal core of gene regulatory networks
7thSpace Interactive (press release)
In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization …
and more »

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10 Very Interesting People (VIP) in Data Mining

Gregory Piatetsky: Author of the most popular newsletter in the data mining community, he has recently updated his website with new content. You can now subscribe with RSS and you can find KDnuggets on Twitter. Gregory does an amazing job in collecting data mining related information, analyzing it and distributing it to data miners (website).

Bruce Ratner: He is author and his website contains several articles about data mining. He has recently been very active on social networks such as LinkedIn (website).

Ajay Ohri: I think this is the most active blogger in the data mining field. He is very active on many social networks and has an excellent collection of interviews with key people in data mining and related fields (blog).

Vincent Granville: As the creator of AnalyticBridge, Vincent has made a great job in building a community of people specialized in analytics fields. His network links more than 6600 members. So, it’s time to subscribe! (website).

Matthew Hurst: He is the author of the very famous blog “Data Mining: Text Mining, Visualization and Social Media”. He is very active on his blog on topics such as social media and data mining the blogosphere (blog, twitter).

Dean Abbott & Will Dwinnell: I put them together since they are co-bloggers. Abbott’s Analytics is an excellent blog (one of my favorite) related to data mining. When reading the posts, you can really feel the experience of the authors (blog).

Greg Linden: His famous blog – Geeking with Greg – is well known for a while now. He writes very informative posts about personalization related topics (blog).

Matt Cuts: He mainly writes about Google stuffs and SEO. However, he is also well know in the data mining world since several posts are directly or indirectly related to this field (blog).

Themos Kalafatis: He writes a lot about text mining (social network mining, etc.) and his posts are very practical. It is always a pleasure to read his blog (blog, twitter).

Randall Matignon: He is the author of very comprehensive books on SAS Enterprise Miner. You can find all information about his books on his webpage (website).

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