skip to main content
10.1145/775047.775055acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
Article

Querying multiple sets of discovered rules

Published:23 July 2002Publication History

ABSTRACT

Rule mining is an important data mining task that has been applied to numerous real-world applications. Often a rule mining system generates a large number of rules and only a small subset of them is really useful in applications. Although there exist some systems allowing the user to query the discovered rules, they are less suitable for complex ad hoc querying of multiple data mining rulebases to retrieve interesting rules. In this paper, we propose a new powerful rule query language Rule-QL for querying multiple rulebases that is modeled after SQL and has rigorous theoretical foundations of a rule-based calculus. In particular, we first propose a rule-based calculus RC based on the first-order logic, and then present the language Rule-QL that is at least as expressive as the safe fragment of RC. We also propose a number of efficient query evaluation techniques for Rule-QL and test them experimentally on some representative queries to demonstrate the feasibility of Rule-QL.

Index Terms

  1. Querying multiple sets of discovered rules

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
            July 2002
            719 pages
            ISBN:158113567X
            DOI:10.1145/775047

            Copyright © 2002 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 23 July 2002

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • Article

            Acceptance Rates

            KDD '02 Paper Acceptance Rate44of307submissions,14%Overall Acceptance Rate1,133of8,635submissions,13%

            Upcoming Conference

            KDD '24

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader