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STAR: Summarizing Timed Association Rules

Published:03 January 2021Publication History
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Abstract

Timed association rules (TARs) generalize classical association rules (ARs) so that we can express temporal dependencies of the form “If X is true at time t, then Y will likely be true at time (t+τ).” As with ARs, solving the TAR mining problem can generate huge numbers of rules. We show that methods to summarize ARs cannot work directly with TARs, and we develop two notions—strong and weak summaries—to summarize a set of TARs. We show that the problems of finding strong/weak summaries are NP-hard, and we provide polynomial-time approximation algorithms. We show experimentally that the coverage provided by our summarization methods is very high. Both technical measures based on coverage and human experiments on six World Bank datasets using 100 subjects from Mechanical Turk and a separate experiment with terrorism experts on a terrorism dataset show that while both summarization methods perform well, weak summaries are preferred, despite their taking more time to compute than strong summaries.

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        • Published in

          cover image ACM/IMS Transactions on Data Science
          ACM/IMS Transactions on Data Science  Volume 2, Issue 1
          Survey Paper, Special Issue on Urban Computing and Smart Cities and Regular Paper
          February 2021
          167 pages
          ISSN:2691-1922
          DOI:10.1145/3446658
          Issue’s Table of Contents

          Copyright © 2021 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 3 January 2021
          • Accepted: 1 August 2020
          • Revised: 1 July 2020
          • Received: 1 November 2019
          Published in tds Volume 2, Issue 1

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