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We investigate the general model of mining associations in a temporal database, where the exhibition periods of items are allowed to be different from one to another. The database is divided into partitions according to the time granularity imposed. Such temporal association rules allow us to...
We study a problem of mining frequently occurring periodic patterns with a gap requirement from sequences. Given a character sequence S of length L and a pattern P of length l , we consider P a frequently occurring pattern in S if the probability of observing P given a randomly picked length- l...
Prediction errors from a linear model tend to be larger when extrapolation is involved, particularly when the model is wrong. This article considers the problem of extrapolation and interpolation errors when a linear model tree is used for prediction. It proposes several ways to curtail the size...
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