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A context-aware data mining process model based framework for supporting evaluation of data mining results

机译：基于上下文感知的数据挖掘过程模型的框架，用于支持对数据挖掘结果的评估

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摘要

The knowledge discovery via data mining process (KDDM) is a multiple phase that aims to at a minimum semi-automatically extract new knowledge from existing datasets. For many data mining tasks, the evaluation phase is a challenging one for various reasons. Given this challenge several studies have presented techniques that could be used for the semi-automated evaluation of data mining results. When taken together, these studies suggest the possibility of a common multi-criteria evaluation framework. The use of such a multi-criteria evaluation framework, however, requires that relevant objectives, measures and preference function be identified. This implies that the context of the DM problem is particularly important for the evaluation phase of the KDDM process. Our framework utilizes and integrates a pair of established tightly coupled techniques (i.e. Value Focused Thinking (VFT) and the Goal-Question-Metric (GQM) methods) as well as established techniques from multi-criteria decision analysis in order to explicate and utilize context information in order to facilitate semi-automated evaluation.
机译：通过数据挖掘过程（KDDM）进行的知识发现是一个多个阶段，旨在最小程度地从现有数据集中半自动提取新知识。对于许多数据挖掘任务，由于各种原因，评估阶段是一个具有挑战性的阶段。面对这一挑战，几项研究提出了可用于数据挖掘结果的半自动评估的技术。综合起来，这些研究表明了建立通用的多标准评估框架的可能性。但是，使用这种多标准评估框架需要确定相关的目标，措施和偏好功能。这意味着DM问题的上下文对于KDDM流程的评估阶段特别重要。我们的框架利用并整合了一对已建立的紧密耦合的技术（即“价值聚焦思维”（VFT）和“目标-问题-度量”（GQM）方法）以及多准则决策分析中已建立的技术，以阐明和利用上下文信息以便于半自动化评估。

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