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An unsupervised neural network approach to predictive data mining

机译:一种无监督的神经网络预测数据挖掘方法

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摘要

Rule mining is one of the popular data mining (DM) methods since rules provide concise statements of potentially important information that is easily understood by end users and are also useful patterns for predictive data mining (PDM). This paper proposes rule mining methods using an unsupervised neural network approach. Two methods are adopted based on the way of unsupervised neural networks are applied in rule mining models. In the first method, the unsupervised neural network is used for clustering, which provides class information to the rule mining process. In the second method, automated rule mining takes the place of trained neurons as it grows in a hierarchical structure of unsupervised neural network.
机译:规则挖掘是流行的数据挖掘(DM)方法之一,因为规则提供了潜在重要信息的简洁说明,这些信息很容易被最终用户理解,并且对于预测数据挖掘(PDM)也是有用的模式。本文提出了一种使用无监督神经网络方法的规则挖掘方法。基于无监督神经网络在规则挖掘模型中的应用,采用了两种方法。在第一种方法中,将无监督神经网络用于聚类,从而为规则挖掘过程提供类信息。在第二种方法中,当规则神经在无监督神经网络的分层结构中增长时,它会取代训练有素的神经元。

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