Affiliations: [a] Institute of Information Engineering, I-Shou University, Kaohsiung 840, Taiwan. Tel.: +886 7 3635377; E-mail: [email protected] | [b] Department of Information Management, I-Shou University, Kaohsiung 840, Taiwan. E-mail: [email protected]
Correspondence:
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Corresponding author.
Abstract: Mining generalized association rules among items in the presence of taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or items within the same taxonomy level. This constraint would restrain an expert from discovering more interesting but much less supported association rules. In our previous work, we have addressed this problem and proposed two algorithms, MMS_Cumulate and MMS_Stratify. In this paper, we examined the problem of maintaining the discovered multi-supported, generalized association rules when new transactions are added into the original database. We proposed two algorithms, UD_Cumulate and UD_Stratify, which can incrementally update the discovered generalized association rules with non-uniform support specification and are capable of effectively reducing the number of candidate sets and database re-scanning. Empirical evaluation showed that UD_Cumulate and UD_Stratify are 2–6 times faster than running MMS_Cumulate or MMS_Stratify on the updated database afresh.
