Data sets and other data web pages from Lynn Eberly's website at UMN biostat

Data sets and other data web pages from Lynn Eberly's
website at UMN biostat

R packages with interesting datasets

The asuR Package:
Functions and data sets for a lecture in Advanced Statistics using R. Especially the functions mancontr() and inspect() may be of general interest.
The alr3 package
The faraway package

Clustering Music Clips

Data from

http://www.public.iastate.edu/~dicook/stat503/music-plusnew-sub-full.csv

1 Description [ From Di Cook's website]
This data was collected by Dr Cook from her own CDs. Using a Mac she read the track into the music editing software Amadeus II, snipped and saved the first 40 seconds as a WAV file. (WAV is an audio format developed by Microsoft, commonly used on Windows but it is getting less popular.) These files were read into R using the package tuneR. This converts the audio file into numeric data. All of the CDs contained left and right channels, and variables were calculated on both channels. The resulting data has 62 rows (cases)
and 7 columns (variables).

• LVar, LAve, LMax: average, variance, maximum of the frequencies of the left channel.
• LFEner: an indicator of the amplitude or loudness of the sound.
• LFreq: Median of the location of the 15 highest peak in the periodogram.

There are 11 tracks by Abba, 11 from the Beatles and 10 the Eels, which would be considered to be Rock, and 13 tracks by Vivaldi, 6 of Mozart and 8 of Beethoven, considered to be Classical. There are also 3 tracks from Enya, considered to be New Wave. The main question we want to answer is:

“Can we group the tracks into a small number of clusters according to their similarity on audio charactieristics?”

This information might be used to arrange tracks on a digital music player. Other questions of interest might
be:
• Do the rock tracks have different characteristics than classical tracks?
• How does Enya compare to rock and classical tracks?
• Are there differences between the tracks of different artists?

The Coma Supercluster

Still in need for actual dataset

The Coma supercluster is a very famous supercluster. This map below is a plot of the brightest galaxies (from the Principal Galaxies Catalogue) in this region of the sky. Dominating this picture is the Virgo cluster - the nearest rich cluster in the universe and the dominant cluster in the the Virgo supercluster. Above the Virgo cluster and much further away are two much richer clusters - A1367 and A1656. These are the two main clusters in the Coma supercluster.

Datasets for Statistical Analysis

Datasets for Statistical Analysis by Peter Dunn
Great resource of datasets both from book and indexed by methods

• Datasets from books
• Techniques index
• Applications index
• Reported statistics

vehicle Sillhoute ( Clustering)

• ftp://ftp.ics.uci.edu/pub/machine-learning-databases
• http://www.ics.uci.edu/~mlearn/MLRepository.html

Spam Dataset for classification

1. Title:  SPAM E-mail Database

2. Sources:
 (a) Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt
      Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304
 (b) Donor: George Forman (gforman at nospam hpl.hp.com)  650-857-7835
 (c) Generated: June-July 1999

3. Past Usage:
 (a) Hewlett-Packard Internal-only Technical Report. External forthcoming.
 (b) Determine whether a given email is spam or not.
 (c) ~7% misclassification error.
     False positives (marking good mail as spam) are very undesirable.
     If we insist on zero false positives in the training/testing set,
     20-25% of the spam passed through the filter.

4. Relevant Information:
      The "spam" concept is diverse: advertisements for products/web
      sites, make money fast schemes, chain letters, pornography...
 Our collection of spam e-mails came from our postmaster and
 individuals who had filed spam.  Our collection of non-spam
 e-mails came from filed work and personal e-mails, and hence
 the word 'george' and the area code '650' are indicators of
 non-spam.  These are useful when constructing a personalized
 spam filter.  One would either have to blind such non-spam
 indicators or get a very wide collection of non-spam to
 generate a general purpose spam filter.

      For background on spam:
      Cranor, Lorrie F., LaMacchia, Brian A.  Spam!
      Communications of the ACM, 41(8):74-83, 1998.

http://www.ics.uci.edu/~mlearn/databases/spambase/