*
，
（华北电力大学电子与通信工程系 保定
071000
）
：
，
-
h-
：
h-
10.3969／j.issn.1000-0801.2014.10.017
Gao Huisheng
Li Yingmin
（Department of Electronic and Communication Engineering of North China Electric Power University， Baoding 071000， China）
The traditional association rules mining algorithms preferred to applying single sliding time window， and
the mined alarm association rules were high support-high confidence. To solve this problem
a m e t hod was proposed
to calculate comprehensive related confidence between the time adjacent items by the confidence fusion algorithm.
Then gene ra t ed alarm transactions were generated based on the calcula t e d results， which enhanced alarm correlation
within a transaction. The proposed approach can reduce the choice of support requirement and mine low
support-high confidence association rules effectively. Based on h-confidence theory， interesting alarm association
rules were screened. Experimental results showed the feasibility and effectiveness of this approach.
alarm association rule， comprehensive related confidence， h-confidence theory
*
No.13XS32
1
WINEPI
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WINEPI
1
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，
［3］
Spatio-Temporal Apriori
110
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