Using Sub-sequence Patterns for Detecting Organ Trafficking Websites

  • Suraj Jung Pandey
  • Suresh Manandhar
  • Agnieszka Kleszcz
Part of the Communications in Computer and Information Science book series (CCIS, volume 368)


This paper presents a novel method for mining suspicious websites from World Wide Web by using state-of-the-art pattern mining and machine learning methods. In this document, the term “suspicious website” is used to mean any website that contains known or suspected violations of law. Although, we present our evaluation on illegal online organ trading, the method described in this paper is generic and can be used to detect any specific kind of websites. We use an iterative setting in which at each iterations we unearth both normal and suspicious websites. These newly detected websites are augmented in our training examples and used in next iterations. The first iteration uses user supplied seed normal and suspicious websites. We show that the accuracy increases in intial iterations but decreases with further increase in iterations. This is due to the bias caused by adding large number of normal websites and also due to the automatic addition of noise in training examples.


Support Vector Machine Text Mining Pattern Mining Word Sense Disambiguation Computational Linguistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Suraj Jung Pandey
    • 1
  • Suresh Manandhar
    • 1
  • Agnieszka Kleszcz
    • 2
  1. 1.University of YorkHeslingtonUK
  2. 2.AGH University of Science and TechnologyKrakowPoland

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