Discovery of association rules in tabular data

G. Richards; V. J. Rayward-Smith

doi:10.1109/ICDM.2001.989553

Discovery of association rules in tabular data

G. Richards, V. J. Rayward-Smith

School of Computing Sciences

Research output: Contribution to conference › Paper

8 Citations (Scopus)

Abstract

In this paper we address the problem of finding all association rules in tabular data. An algorithm, ARA, for finding rules, that satisfy clearly specified constraints, in tabular data is presented. ARA is based on the dense miner algorithm but includes an additional constraint and an improved method of calculating support. ARA is tested and compared with our implementation of dense miner; it is concluded that ARA is usually more efficient than dense miner and is often considerably more so. We also consider the potential for modifying the constraints used in ARA in order to find more general rules

Original language	English
Pages	465-473
Number of pages	9
DOIs	https://doi.org/10.1109/ICDM.2001.989553
Publication status	Published - 2001
Event	IEEE First International Conference on Data Mining - San Jose, United States Duration: 29 Nov 2001 → 2 Dec 2001

Conference

Conference	IEEE First International Conference on Data Mining
Country/Territory	United States
City	San Jose
Period	29/11/01 → 2/12/01

Access to Document

10.1109/ICDM.2001.989553

Cite this

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AB - In this paper we address the problem of finding all association rules in tabular data. An algorithm, ARA, for finding rules, that satisfy clearly specified constraints, in tabular data is presented. ARA is based on the dense miner algorithm but includes an additional constraint and an improved method of calculating support. ARA is tested and compared with our implementation of dense miner; it is concluded that ARA is usually more efficient than dense miner and is often considerably more so. We also consider the potential for modifying the constraints used in ARA in order to find more general rules

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