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@INPROCEEDINGS{russ2007relfeedback,
        author = {Ru{\ss}, Georg and Kruse, Rudolf and Nauck, Detlef and B{\"{o}}ttcher, Mirko},
        editor = {Bramer, Max},
         month = jan,
         title = {Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval},
     booktitle = {Research and Development in Intelligent Systems},
        series = {Proceedings of AI-2007},
        volume = {24},
          year = {2008},
         pages = {253--266},
     publisher = {Springer},
      location = {Cambridge},
  organization = {BCS SGAI},
           url = {http://www.springerlink.com/content/v214143743450884/},
           doi = {10.1007/978-1-84800-094-0_19},
      abstract = {The task of detecting those association rules which are interesting
within the vast set of discovered ones still is a major research challenge
in data mining. Although several possible solutions have been proposed,
they usually require a user to be aware what he knows, to have a rough
idea what he is looking for, and to be able to specify this knowledge in
advance. In this paper we compare the task of finding the most relevant
rules with the task of finding the most relevant documents known from
Information Retrieval. We propose a novel and flexible method of rel-
evance feedback for association rules which leverages technologies from
Information Retrieval, like document vectors, term frequencies and simi-
larity calculations. By acquiring a user's preferences our approach builds
a repository of what he considers to be (non-)relevant. By calculating
and aggregating the similarities of each unexamined rule with the rules
in the repository we obtain a relevance score which better reflects the
user's notion of relevance with each feedback provided.}
}