Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval
| Type of publication: | Inproceedings |
| Citation: | russ2007relfeedback |
| Booktitle: | Research and Development in Intelligent Systems |
| Series: | Proceedings of AI-2007 |
| Volume: | 24 |
| Year: | 2008 |
| Month: | January |
| Pages: | 253--266 |
| Publisher: | Springer |
| Location: | Cambridge |
| Organization: | BCS SGAI |
| URL: | http://www.springerlink.com/co... |
| 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. |
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| Added by: | [ADM] |
| Total mark: | 0 |
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