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中文摘要：
该文在研究粒计算理论的基础上,提出了一种基于粒计算的增量式知识获取方法。该方法通过建立决策信息系统原始的知识粒树,对新增数据,在原始知识粒树中查找相匹配的知识粒,并依据决策值更新知识粒树,实现快速高效地处理动态信息系统。算法分析及实验对比结果表明,该方法在动态信息系统知识获取方面优于RGAGC和ID4方法。
英文摘要：
A new incremental knowledge acquisition method based on granular computing theory is proposed.First,an original knowledge granule tree is established according to the decision-making information system.Then,for any new additional data,its matched knowledge granule in original knowledge granule tree is found at first,and then the original knowledge granule tree is updated according to the corresponding decision-making value.The new method is an efficient tool for processing dynamic data information.Both algorithm analysis and experiment results show that the new method for processing dynamic information systems and acquiring corresponding rules is superior to RGAGC and ID4 respectively.
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