论文标题
从基于间隔的事件序列中挖掘高级效用模式的投影上限
A Projected Upper Bound for Mining High Utility Patterns from Interval-Based Event Sequences
论文作者
论文摘要
高实用图案挖掘是一个有趣但充满挑战的问题。如果寻求解决溶液的功效，则该问题的内在计算成本将面临进一步的挑战。最近，对基于间隔的事件序列进行了研究，并限制了模式的长度和大小。但是，拟议的解决方案缺乏足够的效率。为了解决这个问题，我们提出了一个预测的上限，该上限是从基于间隔的事件序列发现的模式的实用性。为了显示其有效性，上限是由Huipminer算法采用的修剪策略来利用的。实验结果表明，新的上限在执行时间和内存使用方面都改善了Huipminer的性能。
High utility pattern mining is an interesting yet challenging problem. The intrinsic computational cost of the problem will impose further challenges if efficiency in addition to the efficacy of a solution is sought. Recently, this problem was studied on interval-based event sequences with a constraint on the length and size of the patterns. However, the proposed solution lacks adequate efficiency. To address this issue, we propose a projected upper bound on the utility of the patterns discovered from sequences of interval-based events. To show its effectiveness, the upper bound is utilized by a pruning strategy employed by the HUIPMiner algorithm. Experimental results show that the new upper bound improves HUIPMiner performance in terms of both execution time and memory usage.
