An Active Candidate Set Management Model for Realtime Association Rule Discovery 


Vol. 9,  No. 2, pp. 215-226, Apr.  2002
10.3745/KIPSTD.2002.9.2.215


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  Abstract

Considering the rapid process of media's breakthrough and diverse patterns of consumption's analysis, a uniform analysis might be much rooms to be desired for interpretation of new phenomena. In special, the products happening intensive sails on around an anniversary or fresh food have the restricted marketing hours. Moreover, traditional association rule discovery algorithms might not be appropriate for analysis of sales pattern given in a specific time because existing approaches require iterative scan operation to find association rule in large scale transaction databases. In this paper, we propose an incremental candidate set management model based on twin-hashing technique to find association rule in special sales pattern using database trigger and stored procedure. We also prove performance of the proposed model through implementation and experiment.

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[IEEE Style]

Y. H. Shin and K. H. Ryu, "An Active Candidate Set Management Model for Realtime Association Rule Discovery," The KIPS Transactions:PartD, vol. 9, no. 2, pp. 215-226, 2002. DOI: 10.3745/KIPSTD.2002.9.2.215.

[ACM Style]

Ye Ho Shin and Keun Ho Ryu. 2002. An Active Candidate Set Management Model for Realtime Association Rule Discovery. The KIPS Transactions:PartD, 9, 2, (2002), 215-226. DOI: 10.3745/KIPSTD.2002.9.2.215.

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