One click mining: interactive local pattern discovery through implicit preference and performance learning

Mario Boley, M. Mampaey, Bo Kang, P. Tokmakov, S. Wrobel
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引用次数: 67

Abstract

It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, all its required parameters, and a corresponding algorithm. This hinders the desired rapid interaction---especially with users that are experts of the data domain rather than data mining experts. In this paper, we present a fundamentally new approach towards user involvement that relies exclusively on the implicit feedback available from the natural analysis behavior of the user, and at the same time allows the user to work with a multitude of pattern classes and discovery algorithms simultaneously without even knowing the details of each algorithm. To achieve this goal, we are relying on a recently proposed co-active learning model and a special feature representation of patterns to arrive at an adaptively tuned user interestingness model. At the same time, we propose an adaptive time-allocation strategy to distribute computation time among a set of underlying mining algorithms. We describe the technical details of our approach, present the user interface for gathering implicit feedback, and provide preliminary evaluation results.
一键挖掘:通过隐式偏好和性能学习进行交互式本地模式发现
众所周知,从数据中发现的生产性模式必须尽可能直接地与用户交互。最先进的工具箱需要对复杂的工作流进行规范,并明确选择数据挖掘方法、其所需的所有参数和相应的算法。这阻碍了期望的快速交互——特别是与数据领域的专家而不是数据挖掘专家的用户之间的交互。在本文中,我们提出了一种全新的用户参与方法,该方法完全依赖于用户自然分析行为提供的隐式反馈,同时允许用户同时使用多种模式类和发现算法,甚至不知道每种算法的细节。为了实现这一目标,我们依靠最近提出的协同学习模型和模式的特殊特征表示来达到自适应调整的用户兴趣模型。同时,我们提出了一种自适应时间分配策略,将计算时间分配到一组底层挖掘算法中。我们描述了我们的方法的技术细节,展示了收集隐式反馈的用户界面,并提供了初步的评估结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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