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I am looking for some medium to long length papers/websites/etc about data mining, specifically where one dataset is explored in depth from data preparation through final model. I am particularly interested in discussions about application of machine learning algos and also basic data modeling. An example would be Luis Torgo's book 'Data Mining with R'. Any suggestions would be appreciated.

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    $\begingroup$ Are you interested in all methods of data mining, or just one method in particular? $\endgroup$ – Michelle Feb 4 '12 at 1:33
  • $\begingroup$ I'll take whatever I can get. $\endgroup$ – screechOwl Feb 5 '12 at 17:22
  • $\begingroup$ I'm kind of looking for more specific stuff about how to walk thru a specific data set. Some of the KDD Cup write-ups by winning teams are along the lines of what I'm looking for. Basically narratives of how to deal with issues specific to a given dataset. The analogy would be to a case study in business school where one situation at one company is discussed in great detail. $\endgroup$ – screechOwl Feb 14 '12 at 18:00
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Check out the Kaggle.com blog, where winners discuss their approaches to solving a data mining competition. You can then go back to the kaggle.com website to get the description and data and try it out yourself.

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Here's a good place to start:

Top 10 Algorithms in Data Mining

Not much in terms of data preparation in there, but plenty on applications. And lots of good links to relevant papers to read.

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I recommend You articles from free Journal of Statistical Software.

You can find there different applications of data mining/machine learning together with analysis of real data examples. Most articles are about R packages so you can also simultaneously perform their analyses in R. Articles in journal also include R code and packages in R include data.

All data are analyzed in depth there so it is very worthy source for me.

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The caret R package has a set of four vignettes that walk through applying various data preparation tasks, supervised learning algorithms, feature selection, and data visualizations starting from some raw example datasets.

Even though the focus is on how to do these things using functionality provided by caret itself, it's still generally applicable and pretty good reading for real-world projects.

Here are direct links to the four PDF vignettes:

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Here are some that I've found helpful:

KDD Cup 2008 and the Workshop on Mining Medical Data

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