Relevance Feedback for Association Rules using Fuzzy Score Aggregation
| Type of publication: | Inproceedings |
| Citation: | russ2007nafips |
| Booktitle: | Proc. Conf. North American Fuzzy Information Processing Society (NAFIPS 2007) |
| Year: | 2007 |
| Month: | June |
| Pages: | 54--59 |
| URL: | http://ieeexplore.ieee.org/xpl... |
| 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. |
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| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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