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@INPROCEEDINGS{russ2007nafips,
     author = {Ru{\ss}, Georg and B{\"{o}}ttcher, Mirko and Kruse, Rudolf},
      month = jun,
      title = {Relevance Feedback for Association Rules using Fuzzy Score Aggregation},
  booktitle = {Proc. Conf. North American Fuzzy Information Processing Society (NAFIPS 2007)},
       year = {2007},
      pages = {54--59},
        url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=4271033&isnumber=4271017},
        doi = {10.1109/nafips.2007.383810},
   abstract = {We propose a novel and more flexible relevance feedback for association rules which is based on a fuzzy notion of relevance. Our approach transforms association rules into a vector-based representation using some inspiration from document vectors in information retrieval. These vectors are used as the basis for a relevance feedback approach which builds a knowledge base of rules previously rated as (un)interesting by a user. Given an association rule the vector representation is used to obtain a fuzzy score of how much this rule contradicts a rule in the knowledge base. This yields a set of relevance scores for each assessed rule which still need to be aggregated. Rather than relying on a certain aggregation measure we utilize OWA operators for score aggregation to gain a high degree of flexibility and understandability.}
}