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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Exploratory Visual Sequence Mining Based on Pattern-Growth
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Exploratory Visual Sequence Mining Based on Pattern-Growth

机译：基于模式增长的探索性视觉序列挖掘

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摘要

Sequential pattern mining finds applications in numerous diverging fields. Due to the problem's combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user's perspective. Second, as datasets grow, mining large numbers of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a "transparent box" execution model. We propose a novel approach to interactive visual sequence mining that allows the user to guide the execution of a pattern-growth algorithm at suitable points through a powerful visual interface. Our approach (1) introduces the possibility of using local constraints during the mining process, (2) allows stepwise visualization of patterns being mined, and (3) enables the user to steer the mining algorithm towards directions of interest. The use of local constraints significantly improves users' capability to progressively refine the search space without the need to restart computations. We exemplify our approach using two event sequence datasets; one composed of web page visits and another composed of individuals' activity sequences.
机译：顺序模式挖掘在许多不同的领域中都有应用。由于问题的组合性质，出现了两个主要挑战。首先，现有算法输出大量模式，从用户的角度来看，其中许多模式并不有趣。其次，随着数据集的增长，挖掘大量模式会增加计算量。因此，需要挖掘方法，该方法可以将模式搜索集中在感兴趣的方向上。这项工作通过将交互式可视化与顺序模式挖掘相结合来解决此问题，以便创建“透明框”执行模型。我们提出了一种新颖的交互式可视序列挖掘方法，该方法允许用户通过功能强大的可视界面在适当的位置引导模式增长算法的执行。我们的方法（1）引入了在挖掘过程中使用局部约束的可能性，（2）允许逐步可视化正在挖掘的模式，并且（3）使用户能够将挖掘算法引向感兴趣的方向。局部约束的使用极大地提高了用户逐步重新定义搜索空间的能力，而无需重新启动计算。我们使用两个事件序列数据集来举例说明我们的方法。一个由网页访问组成，另一个由个人活动序列组成。

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