PARAS:关联规则挖掘的交互式参数空间探索

Abhishek Mukherji, Xika Lin, Christopher R. Botaish, Jason Whitehouse, Elke A. Rundensteiner, M. Ward, Carolina Ruiz
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引用次数: 7

摘要

我们展示了我们的PARAS技术,用于支持接近实时速度的交互式关联挖掘。将展示PARAS的关键技术创新,特别是稳定的区域抽象和规则冗余管理,支持新的以参数空间为中心的探索性查询。观众将能够交互式地探索规则的参数空间视图。他们将体验到PARAS实现的接近实时的操作速度,例如比较使用不同参数值挖掘的规则集,否则将花费数小时的计算和大量的人工调查。总之,我们将演示PARAS系统通过参数调优建议为数据分析人员提供丰富的体验,同时显著减少试错交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PARAS: interactive parameter space exploration for association rule mining
We demonstrate our PARAS technology for supporting interactive association mining at near real-time speeds. Key technical innovations of PARAS, in particular, stable region abstractions and rule redundancy management supporting novel parameter space-centric exploratory queries will be showcased. The audience will be able to interactively explore the parameter space view of rules. They will experience near real-time speeds achieved by PARAS for operations, such as comparing rule sets mined using different parameter values, that would otherwise take hours of computation and much manual investigation. Overall, we will demonstrate that the PARAS system provides a rich experience to data analysts through parameter tuning recommendations while significantly reducing the trial-and-error interactions.
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