Rough Set Theory : A new Mathematical Approach to Data Analysis
Rough Set Theory : A new Mathematical Approach to Data Analysis
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发表时间：
2008
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通讯作者：
Zdziss Law Pawlak
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作者：
Zdziss Law Pawlak
粗糙集理论是一种新的数据分析方法，也被称为数据挖掘。近年来，我们目睹了快速增长的兴趣，粗糙集理论及其应用，世界各地。粗糙集方法似乎对人工智能和认知科学至关重要，特别是在机器学习，知识获取，决策分析，数据库知识发现，专家系统，归纳推理和模式识别等领域。粗糙集理论的主要优点是它不需要统计学中的概率、Dempster-Shafer理论中的基本概率赋值和模糊集理论中的隶属度或可能性值等数据的任何预先或附加信息。此外，该理论允许直接解释结果。粗糙集理论已经成功地应用于医学、药理学、工程、银行、金融和市场分析等领域的许多实际问题。本文讨论了粗糙集理论的基本概念。所提出的方法对于许多现实生活中的应用来说太简单了，因此它在许多方面进行了扩展，但我们将不在这里讨论这些扩展。有关粗糙集及其应用的更多信息可以在参考文献中找到。
Rough set theory is a new mathematical approach to data analysis recently known also as data mining. In recent years we witnessed a rapid grow of interest in rough set theory and its applications, worldwide. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition. The main advantage of rough set theory is that it does not need any preliminary or additional information about data – like probability in statistics or basic probability assignment in Dempster-Shafer theory and grade of membership or the value of possibility in fuzzy set theory. Besides, the theory allows straightforward interpretation of results. Rough set theory has been successfully applied in many real-life problems in medicine, pharmacology, engineering, banking, financial and market analysis and others. This paper discusses basic concepts of rough set theory. The presented approach is too simple to many real-life applications, therefore it was extended in many ways, but we will not discuss these extensions here. More about rough sets and their applications can be found in the references.