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Proceedings Paper

IRIS: our prototype rule generation system
Author(s): Lisa Singh; Peter Scheuermann; Bin Chen
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Paper Abstract

Our goal is to design a knowledge discovery tool that has the ability to accurately generate rules, using concepts and structured data values, extracted from semi-structured documents. To date, two of our major contributions have been the design of a system architecture that facilitates the discovery of rules from HTML documents, and the development of an efficient association rule algorithm that generates rule sets, based on user specified constraints. This paper discusses each of these contributions within the framework of our prototype system IRIS. IRIS allows users to specify a set of constraints associated with a particular domain and then generates association rules based on these constraints. One of the unique features of IRIS, is that it generates rules using the more structured component of the HTML documents, as well as the conceptual knowledge extracted from the unstructured blocks of text.

Paper Details

Date Published: 25 February 1999
PDF: 8 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339992
Show Author Affiliations
Lisa Singh, Northwestern Univ. (United States)
Peter Scheuermann, Northwestern Univ. (United States)
Bin Chen, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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