Robustness Issues in Text Mining

  • Marco Turchi
  • Domenico Perrotta
  • Marco Riani
  • Andrea Cerioli
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)

Abstract

We extend the Forward Search approach for robust data analysis to address problems in text mining. In this domain, datasets are collections of an arbitrary number of documents, which are represented as vectors of thousands of elements according to the vector space model. When the number of variables v is so large and the dataset size n is smaller by order of magnitudes, the traditional Mahalanobis metric cannot be used as a similarity distance between documents. We show that by monitoring the cosine (dis)similarity measure with the Forward Search approach it is possible to perform robust estimation for a document collection and order the documents so that the most dissimilar (possibly outliers, for that collection) are left at the end. We also show that the presence of more groups of documents in the collection is clearly detected with multiple starts of the Forward Search.

Keywords

Cosine similarity document classification forward search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Turchi
    • 1
  • Domenico Perrotta
    • 1
  • Marco Riani
    • 2
  • Andrea Cerioli
    • 2
  1. 1.European CommissionJoint Research CentreBrusselsBelgium
  2. 2.Department of EconomicsUniversity of ParmaParmaItaly

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