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Data Mining

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Recommendation Systems in Software Engineering

Abstract

Recommendation systems find and summarize patterns in the structure of some data or in how we visit that data. Such summarizing can be implemented by data mining algorithms. While the rest of this book focuses specifically on recommendation systems in software engineering, this chapter provides a more general tutorial introduction to data mining.

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Notes

  1. 1.

    Given a mean value for x over n measurements \(\bar{x} = \frac{1} {n}\sum _{i=1}^{n}x_{i}\), then the total sum of squares is \(\mathit{SS}_{\text{tot}} =\sum _{i}{(x_{i} -\bar{ x})}^{2}\) and the sum of squares of residuals is \(\mathit{SS}_{\text{err}} =\sum _{i}{(x_{i} - f_{i})}^{2}\). From this, the amount by which x determines f is \({R}^{2} = 1 -\left (\mathit{SS}_{\text{err}}/\mathit{SS}_{\text{tot}}\right )\).

  2. 2.

    But it should be emphasized that this is more an issue in the typical toolkit’s implementation than some fatal flaw with random forests.

  3. 3.

    Note that Farnstrom et al. use n = 1 but this is a parameter that can be tuned. In the next section, we discuss incremental learners where, at least during the initial learning phase, all the data will be anomalous since this learner has never seen anything before. For learning from very few examples, n should be greater than one.

  4. 4.

    It turns out that WHERE (described above in Sect. 3.4.5) is a heuristic method for finding the first domain of PCA. But while PCA takes polynomial time, WHERE runs in linear time. For more on the relationship of WHERE to PCA, see Platt [48].

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Authors and Affiliations

  1. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

    Tim Menzies

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  1. Tim Menzies
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Editors and Affiliations

  1. McGill University, Montréal, Québec, Canada

    Martin P. Robillard

  2. University of Hamburg, Hamburg, Germany

    Walid Maalej

  3. University of Calgary, Calgary, Alberta, Canada

    Robert J. Walker

  4. Microsoft Research, Redmond, Washington, USA

    Thomas Zimmermann

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Menzies, T. (2014). Data Mining. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds) Recommendation Systems in Software Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45135-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-45135-5_3

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