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Representing Data Mining Results

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Introduction to Data Mining for the Life Sciences

Abstract

How many different ways can we represent our data in order to convey its meaning to its intended audience? What is the most effective approach? Is one technique better than another? The answer depends on your data, your audience, and the message you are trying to convey. In this chapter, we provide context for the output of our analyses, discussing very traditional representation methodologies, including tables, histograms, graphs, and plots of various types, as well as some of the more creative approaches that have seen increasing mindshare as vehicles of communication across the Internet. We also include an important technique that spans both the algorithmic and representation concepts – trees and rules – since such techniques can be valuable for both explanation and input to other systems and show that not all representations necessarily need to be graphical.

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Notes

  1. 1.

    These are also known as cross classification tables.

  2. 2.

    Data of T- and B-cell acute lymphocytic leukemia from the Ritz Laboratory at the DFCI and available from the Bioconductor web site.

  3. 3.

    http://evolution.genetics.washington.edu/phylip.html

  4. 4.

    http://www.geneious.com/

  5. 5.

    We are, of course, making an assumption that the data can be easily split accordingly. This may not be possible given the data in our learning dataset. However, if it is, Gini branching will choose this split.

  6. 6.

    This measure is used in ID3 and C4.5.

  7. 7.

    An interesting example relates to the drug Viagra which was initially clinically tested as a heart drug but which…well, you know how that ended up.

  8. 8.

    We shall return to this form of the rule later.

  9. 9.

    Witten and Frank (2005 #11) used the term coverage synonymously with support.

  10. 10.

    This is also often referred to as a transaction. We have elected to continue with our definition of our dataset being analyzed as comprising a set of instances.

  11. 11.

    The algorithm suffers from a number of inefficiencies and trade-offs that have resulted in many other algorithms being proposed. For more details, see the articles cited or search “Apriori algorithm.”

  12. 12.

    Valentin Zacharias, “Development and Verification of Rule-Based Systems – A Survey of Developers”, http://www.cs.manchester.ac.uk/ruleML/presentations/session1paper1.pdf.

  13. 13.

    http://www.sciencedirect.com/science/journal/09507051

  14. 14.

    We can use the R function range(sampleData) to obtain this information.

  15. 15.

    We return to this dataset in Chap. 6.

  16. 16.

    http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/r/heatmap/

  17. 17.

    http://flowingdata.com/category/visualization/network-visualization/

  18. 18.

    We’re going to take some artistic license here. “Network” and “graph” are often used interchangeably in the literature.

  19. 19.

    http://sbml.org/More_Detailed_Summary_of_SBML

  20. 20.

    Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edn. Cohen, et al.

  21. 21.

    Treeview is now available from Google code at http://code.google.com/p/treeviewx/ for the Linux/Unix variant and http://taxonomy.zoology.gla.ac.uk/rod/treeview.html for the Windows and Mac versions.

  22. 22.

    http://www.visual-literacy.org/periodic_table/periodic_table.html

  23. 23.

    http://www.visualcomplexity.com/vc/index.cfm

  24. 24.

    http://flowingdata.com/

  25. 25.

    http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/

  26. 26.

    http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html

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Authors and Affiliations

  1. Cincinnati, OH, USA

    Rob Sullivan

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  1. Rob Sullivan
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Sullivan, R. (2012). Representing Data Mining Results. In: Introduction to Data Mining for the Life Sciences. Humana Press. https://doi.org/10.1007/978-1-59745-290-8_4

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