Here is a great website for sharing workflows – it is called MyExperiment.org and it can also include Work flows from many software.
myExperiment currently has 4742 members, 270 groups, 1842 workflows, 423 files and 173 packs
Could it also include workflow from Red-R from #rstats or Enterprise Miner
so you think u like R, huh. Well it is time to pay it forward.
Message from a dear R blogger, Tal G from Tel Aviv (creator of R-bloggers.com and SAS-X.com)
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Calling R lovers and bloggers – to work together on “The R Programming wikibook”
Posted: 20 Jun 2011 07:05 AM PDT
This post is a call for both R community members and R-bloggers, to come and help make The R Programming wikibook be amazing:
Dear R community member – please consider giving a visit to The R Programming wikibook. If you wish to contribute your knowledge and editing skills to the project, then you could learn how to write in wiki-markup here, and how to edit a wikibook here (you can even use R syntax highlighting in the wikibook). You could take information into the site from the (soon to be) growing list of available R resources for harvesting.
Dear R blogger, you can help The R Programming wikibook by doing the following:
Write to your readers about the project and invite them to join.
Add your blog’s R content as an available resource for other editors to use for the wikibook. Here is how to do that:
First, make a clear indication on your blog that your content is licensed under cc-by-sa copyrights (*see what it means at the end of the post). You can do this by adding it to the footer of your blog, or by writing a post that clearly states that this is the case (what a great opportunity to write to your readers about the project…).
Next, go and add a link, to where all of your R content is located on your site, to the resource page (also with a link to the license post, if you wrote one). For example, since I write about other things besides R, I would give a link to my R category page, and will also give a link to this post. If you do not know how to add it to the wiki, just e-mail me about it (tal.galili@gmail.com).
If you are an R blogger, besides living up to the spirit of the R community, you will benefit from joining this project in that every time someone will use your content on the wikibook, they will add your post as a resource. In the long run, this is likely to help visitors of the site get to know about you and strengthen your site’s SEO ranking. Which reminds me, if you write about this, I always appreciate a link back to my blog
* Having a cc-by-sa copyrights means that you will agree that anyone may copy, distribute, display, and make derivative works based on your content, only if they give the author (you) the credits in the manner specified by you. And also that the user may distribute derivative works only under a license identical to the license that governs the original work.
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Three more points:
1) This post is a result of being contacted by Paul (a.k.a: PAC2), asking if I could help promote “The R Programming wikibook” among R-bloggers and their readers. Paul has made many contributions to the book so far. So thank you Paul for both reaching out and helping all of us with your work on this free open source project.
2) I should also mention that the R wiki exists and is open for contribution. And naturally, every thing that will help the R wikibook will help the R wiki as well.
3) Copyright notice: I hereby release all of the writing material content that is categoriesed in the R category page, under the cc-by-sa copyrights (date: 20.06.2011). Now it’s your turn!
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List of R bloggers who have joined: (This list will get updated as this “group writing” project will progress)
R-statistics blog (that’s Tal…)
Decisionstats.com (That’s me)
……………………………………………………………………………….
3) Copyright notice: I hereby release all of the writing material content of this website, under the cc-by-sa copyrights (date: 21.06.2011). Now it’s your turn!
https://decisionstats.com/privacy-3/
Content Licensing-
This website has all content licensed under
http://creativecommons.org/licenses/by-sa/3.0/
You are free:
to Share — to copy, distribute and transmit the work
to Remix — to adapt the work
If you do a Google search for Data Mining Blog- for the past several years one Blog will come on top. data mining blog – Google Search http://bit.ly/kEdPlE
To honor 5 years of Sandro Saitta’s blog (yes thats 5 years!) , we cover an exclusive interview with him where he reveals his unique sauce for cool techie blogging.
Sandro- My first experience with data mining was my master project. I used decision tree to predict pollen concentration for the following week using input data such as wind, temperature and rain. The fact that an algorithm can make a computer learn from experience was really amazing to me. I found it so interesting that I started a PhD in data mining. This time, the field of application was civil engineering. Civil engineers put a lot of sensors on their structure in order to understand how they behave. With all these sensors they generate a lot of data. To interpret these data, I used data mining techniques such as feature selection and clustering. I started my blog, Data Mining Research, during my PhD, to share with other researchers.
I then started applying data mining in the stock market as my first job in industry. I realized the difference between image recognition, where 99% correct classification rate is state of the art, and stock market, where you’re happy with 55%. However, the company ambiance was not as good as I thought, so I moved to consulting. There, I applied data mining in behavioral targeting to increase click-through rates. When you compare the number of customers who click with the ones who don’t, then you really understand what class imbalance mean. A few months ago, I accepted a very good opportunity at SICPA. I’m looking forward to resolving new challenges there.
Ajay- Your blog is the top ranked blog for “data mining blog”. Could you share some tips on better blogging for analytics and technical people
Sandro- It’s always difficult to start a blog, since at the beginning you have no reader. Writing for nobody may seem stupid, but it is not. By writing my first posts during my PhD I was reorganizing my ideas. I was expressing concepts which were not always clear to me. I thus learned a lot and also improved my English level. Of course, it’s still not perfect, but I hope most people can understand me.
Next come the readers. A few dozen each week first. To increase this number, I then started to learn SEO (Search Engine Optimization) by reading books and blogs. I tested many techniques that increased Data Mining Research visibility in the blogosphere. I think SEO is interesting when you already have some content published (which means not at the very beginning of your blog). After a while, once your blog is nicely ranked, the main task is to work on the content of the blog. To be of interest, your content must be particular: original, informative or provocative for example. I also had the chance to have a good visibility thanks to well-known people in the field like Kevin Hillstrom, Gregory Piatetsky-Shapiro, Will Dwinnell / Dean Abbott, Vincent Granville, Matthew Hurst and many others.
Ajay- Whats your favorite statistical software and what are the various softwares that you have worked with.
Could you compare and contrast these software as well.
Sandro- My favorite software at this point is SAS. I worked with it for two years. Once you know the language, you can perform ETL and data mining so easily. It’s also very fast compared to others. There are a lot of tools for data mining, but I cannot think of a tool that is as powerful as SAS and, in the same time, has a high-level programming language behind it.
I also worked with R and Matlab. R is very nice since you have all the up-to-date data mining algorithms implemented. However, working in the memory is not always a good choice, especially for ETL. Matlab is an excellent tool for prototyping. It’s not so fast and certainly not done for ETL, but the price is low regarding all the possibilities for data mining. According to me, SAS is the best choice for ETL and a good choice for data mining. Of course, there is the price.
Ajay- What are your favorite techniques and training resources for learning basics of data mining to say statisticians or business management graduates.
Sandro- I’m the kind of guy who likes to read books. I read data mining books one after the other. The fact that the same concepts are explained differently (and by different people) helps a lot in learning a topic like data mining. Of course, nothing replaces experience in the field. You can read hundreds of books, you will still not be a good practitioner until you really apply data mining in specific fields. My second choice after books is blogs. By reading data mining blogs, you will really see the issues and challenges in the field. It’s still not experience, but we are closer. Finally, web resources and networks such as KDnuggets of course, but also AnalyticBridge and LinkedIn.
Ajay- Describe your hobbies and how they help you ,if at all in your professional life.
Sandro- One of my hobbies is reading. I read a lot of books about data mining, SEO, Google as well as Sci-Fi and Fantasy. I’m a big fan of Asimov by the way. My other hobby is playing tennis. I think I simply use my hobbies as a way to find equilibrium in my life. I always try to find the best balance between work, family, friends and sport.
Ajay- What are your plans for your website for 2011-2012.
Sandro- I will continue to publish guest posts and interviews. I think it is important to let other people express themselves about data mining topics. I will not write about my current applications due to the policies of my current employer. But don’t worry, I still have a lot to write, whether it is technical or not. I will also emphasis more on my experience with data mining, advices for data miners, tips and tricks, and of course book reviews!
Standard Disclosure of Blogging- Sandro awarded me the Peoples Choice award for his blog for 2010 and carried out my interview. There is a lot of love between our respective wordpress blogs, but to reassure our puritan American readers- it is platonic and intellectual.
About Sandro S-
Sandro Saitta is a Data Mining Research Engineer at SICPA Security Solutions. He is also a blogger at Data Mining Research (www.dataminingblog.com). His interests include data mining, machine learning, search engine optimization and website marketing.
You can contact Mr Saitta at his Twitter address-
https://twitter.com/#!/dataminingblog
In view of the recent root level breach of WordPress, which may include viewing source code for hidden hacks or Trojans, as effective immediately, please Decisionstats.com has no responsibility for any viruses, or Trojans that you may inadvertently download while on this website. I will be responsible for any deliberate malicious honey traps I put up , but any body putting an interesting comment with a link on this website , can and may direct you to phishing.
All disputes will be to subject to the jurisdiction of Tis Hazari Court, Delhi, India as already mentioned.
I am hoping to put this on my pre-ordered or Amazon Wish list. The book the common people who wanted to do data mining with , but were unable to ask aloud they didnt know much. It is written by the seminal Australian authority on data mining Dr Graham Williams whom I interviewed here at https://decisionstats.com/2009/01/13/interview-dr-graham-williams/
Data Mining for the masses using an ergonomically designed Graphical User Interface.
Thank you Springer. Thank you Dr Graham Williams
http://www.springer.com/statistics/physical+%26+information+science/book/978-1-4419-9889-7
The Art of Excavating Data for Knowledge Discovery
Series: Use R
Williams, Graham
1st Edition., 2011, XX, 409 p. 150 illus. in color.
Softcover, ISBN 978-1-4419-9889-7
Due: August 29, 2011
Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing.
The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.
Content Level » Research
Keywords » Data mining
Related subjects » Physical & Information Science
Related- https://decisionstats.com/2009/01/13/interview-dr-graham-williams/
Basically a bar chart shows rectangular bars with length proportional to the quantities being described. It helps to see relative quantities between various category types.
The barplot() command is used for making Bar Plots, while hist() is used for histograms. You can also use the plot() command with type=h to create histograms-The official R manual also suggests that Dot plots using dotchart () are a reasonable substitute for bar plots.
A very simple easy to understand tutorial for basic bar plots is at http://msenux.redwoods.edu/math/R/barplot.php
The difference between the three main functions that can be used for these charts are shown below-
> VADeaths
Rural Male Rural Female Urban Male Urban Female
50-54 11.7 8.7 15.4 8.4
55-59 18.1 11.7 24.3 13.6
60-64 26.9 20.3 37.0 19.3
65-69 41.0 30.9 54.6 35.1
70-74 66.0 54.3 71.1 50.0
> plot(VADeaths,type=”h”)
DECISION STATS 