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  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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FGC: an efficient constraint-based frequent set miner

Pears, R; Kutty, S
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AICCSA2721.pdf (256.0Kb)
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http://hdl.handle.net/10292/5184
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Abstract
Despite advances in algorithmic design, association rule mining remains problematic from a performance viewpoint when the size of the underlying transaction database is large. The well-known a priori approach, while reducing the computational effort involved still suffers from the problem of scalability due to its reliance on generating candidate itemsets. In this paper we present a novel approach that combines the power of preprocessing with the application of user-defined constraints to prune the itemset space prior to building a compact FP-tree. Experimentation shows that that our algorithm significantly outperforms the current state of the art algorithm, FP-bonsai.
Date
2007
Source
2007 ACS/IEEE International Conference on Computer Systems and Applications. AICCSA , Amman, Jordan, published in: Proceedings of the 2007 ACS/IEEE International Conference on Computer Systems and Applicationsm AICCSA, pp.424 - 431
Item Type
Conference Contribution
Publisher
IEEE
DOI
10.1109/AICCSA.2007.370916
Rights Statement
Copyright © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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