We study the incremental data mining technology based outlier factor.
本文主要研究了基于孤立点因子的增量式挖掘技术。
Active incremental data mining is implemented by applying the trigger mechanism of the database to invoke procedures.
主动增量挖掘利用数据库的触发器机制调用存储过程来实现。
After that incremental data mining algorithm of establishing decision matrix to each of decision type is put forward and the characteristic of rationality and validity are examined by example.
在此基础上提出相应的对每一个决策类建立决策矩阵的增量式挖掘算法，最后利用算例验证了算法的合理性和有效性。
Data mining is a new emerging area for the research of artificial intelligence and databases, in which incremental updating of association rules is an important research topic.
数据挖掘是当今国际人工智能和数据库研究的新兴领域，而关联规则的更新是数据挖掘的一个重要研究内容。
But, if we need recalculate rules from all data to mine knowledge after updating the database, it will consume massive resources, which causes the urgent demand of the incremental mining algorithm.
但是，如果在数据库更新之后要对全部数据重新进行挖掘，需要消耗大量的资源，这导致对增量挖掘算法的迫切需求。
This paper research a data mining system based on incremental updating algorithms of rough set theory.
本文主要研究基于粗集的增量算法的数据挖掘系统。
The paper presents an algorithm for incremental mining which is based on pattern decomposing tree and has no need for scanning old data base.
本文提出了一个基于模式分解树，不需要扫描原数据库的增量挖掘算法。
The paper presents an algorithm for incremental mining which is based on pattern decomposing tree and has no need for scanning old data base.
本文提出了一个基于模式分解树，不需要扫描原数据库的增量挖掘算法。
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