Comprehensible Knowledge-Discovery in Databases
Comprehensible Knowledge-Discovery in Databases
复制标题
数据库中可理解的知识发现
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
M. Pazzani
中科院分区：
文献类型：
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作者：
M. Pazzani
科学、商业和医药领域的大型数据库正在被定期收集。已经提出了来自统计学、信号处理、模式识别、机器学习和神经网络的各种技术，以通过发现有用的类别来理解数据。然而，到目前为止，数据挖掘的研究还没有注意到认知因素，使学习的类别可理解。我们表明，影响学习模型的可理解性的一个因素是与现有知识的一致性，并描述了一种学习算法，该算法在考虑到这一目标的情况下创建概念。
Large databases are routinely being collected in science, business and medicines. A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand the data by discovering useful categories. However, to date research in data mining has not paid attention to the cognitive factors that make learned categories comprehensible. We show that one factor that influences the comprehensibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind.