Towards Efficient Re-mining of Frequent Patterns upon Threshold Changes
Mining of frequent patterns has been studied popularly in data mining area. However, very little work has been done on the problem of updating mined patterns upon threshold changes, in spite of its practical benefits. When users interactively mine frequent patterns, one difficulty is how to select an appropriate minimum support threshold. So, it is often the case that they have to continuously tune the threshold. A direct way is to re-execute the mining procedure many times with varied thresholds, which is nontrivial in large database. In this paper, an efficient Extension and Re-mining algorithm is proposed for update of previously discovered frequent patterns upon threshold changes. The algorithm proposed in this paper has been implemented and its performance is compared with re-running FP-growth algorithm under different thresholds. The study shows that our algorithm is significantly faster than the latter, especially when mining long frequent patterns in large databases.
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