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Developing Attention Focus Metrics for Autonomous Hypothesis Generation in Data Mining

机译：开发用于数据挖掘中的自主假设生成的注意力关注指标

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摘要

When facing a data mining task, human experts tend to be responsible for proposing the hypotheses that lead to the discovery of interesting patterns. Recently, there is interest in automating the hypothesis generation process to reduce the load on the human expert during data mining. However, if we want an artificial agent to undertake this new role, we also need new metrics to measure the success of the hypothesis generation mechanism. This paper explores the design of metrics for evaluating hypothesis generation algorithms in terms of differences in the way they focus attention in the data mining search-space. We demonstrate our new metrics applied to three stochastic search based prototype hypothesis generation algorithms. Results show that some differences in attention focus can be identified using our metrics. Directions for further work in attention focus metrics and hypothesis generation algorithms are discussed.
机译：当面对数据挖掘任务时，人类专家往往负责提出导致发现有趣模式的假设。最近，人们对使假设生成过程自动化以减少数据挖掘过程中人类专家的负担产生了兴趣。但是，如果我们希望人工代理承担这一新角色，那么我们还需要新的度量标准来衡量假设生成机制的成功。本文探讨了用于评估假设生成算法的度量标准的设计，这些度量依据它们在数据挖掘搜索空间中关注的方式上的差异。我们展示了适用于三种基于随机搜索的原型假设生成算法的新指标。结果表明，可以使用我们的指标来确定关注焦点上的一些差异。讨论了在关注焦点度量和假设生成算法上进行进一步工作的方向。

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