论文标题
间隔数据和分布数据的封闭模式挖掘
Closed pattern mining of interval data and distributional data
论文作者
论文摘要
我们讨论了用于封闭模式挖掘的模式语言以及间隔数据和分布数据的学习。我们首先介绍依靠基于相交的约束或基于包容性的约束或两者应用于间隔的对成对的模式语言。我们讨论了等间隔模式等项目集的编码，从而允许使用封闭项目集采矿和正式概念分析程序。我们在集群和监督学习任务上实验这些语言。然后，我们展示如何扩展解决分布数据的方法。
We discuss pattern languages for closed pattern mining and learning of interval data and distributional data. We first introduce pattern languages relying on pairs of intersection-based constraints or pairs of inclusion based constraints, or both, applied to intervals. We discuss the encoding of such interval patterns as itemsets thus allowing to use closed itemsets mining and formal concept analysis programs. We experiment these languages on clustering and supervised learning tasks. Then we show how to extend the approach to address distributional data.
