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Prioritization of association rules in data mining: Multiple criteria decision approach

机译：数据挖掘中关联规则的优先级：多准则决策方法

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摘要

Data mining techniques, extracting patterns from large databases are the processes that focus on the automatic exploration and analysis of large quantities of raw data in order to discover meaningful patterns and rules. In the process of applying the methods, most of the managers who are engaging the business encounter a multitude of rules resulted from the data mining technique. In view of multi-faceted characteristics of such rules, in general, the rules are featured by multiple conflicting criteria that are directly related with the business values, such as, e.g. expected monetary value or incremental monetary value. In the paper, we present a method for rule prioritization, taking into account the business values which are comprised of objective metric or managers' subjective judgments. The proposed methodology is an attempt to make synergy with decision analysis techniques for solving problems in the domain of data mining. We believe that this approach would be particularly useful for the business managers who are suffering from rule quality or quantity problems, conflicts between extracted rules, and difficulties of building a consensus in case several managers are involved for the rule selection.
机译：从大型数据库中提取模式的数据挖掘技术是着重于自动探索和分析大量原始数据以发现有意义的模式和规则的过程。在应用这些方法的过程中，大多数从事业务的经理都会遇到数据挖掘技术所产生的众多规则。鉴于此类规则的多方面特征，通常，规则以与业务价值直接相关的多个冲突标准为特征，例如。预期货币价值或增量货币价值。在本文中，我们提出了一种规则优先级排序方法，其中考虑了由客观指标或管理者的主观判断组成的业务价值。所提出的方法是试图与决策分析技术协同作用以解决数据挖掘领域的问题。我们认为，这种方法对于遭受规则质量或数量问题，提取的规则之间存在冲突以及在多个管理者参与规则选择的情况下难以建立共识的业务经理特别有用。

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