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.
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。
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.
针对负荷预测过程中样本数据是滚动更新的特点，在RAPHF的基础上提出了具有动态挖掘能力的粗糙集增量算法raphf - I。
On the basis of RAPHF, a rough set incremental algorithm with dynamic mining ability, namely, RAPHF-I is proposed by considering the updating samples.
本文主要研究基于粗集的增量算法的数据挖掘系统。
This paper research a data mining system based on incremental updating algorithms of rough set theory.
同时针对双事件时态关联规则挖掘提出一个增量算法，并给出了实验结果。
To mine temporal association rules in two events, we propose a new increment algorithm. Moreover, their experiment results are given.
通过实验证明了算法HBEC对解决入侵检测问题是有效的，并且具有很强的增量挖掘能力。
The experiment shows that HBEC is effective to resolve the intrusion detection problem and has the strongly incremental mining ability.
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。
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.
在序列模式的增量式挖掘算法中，IUS算法是目前最为先进的算法。
Of all the incremental mining algorithms, the IUS is the most advanced at present.
提出了一种新的量化关联规则挖掘算法QAR及其增量式更新算法IUQAR。
A novel algorithm, QAR, for mining quantitative association rules and an incremental updating algorithm, IUQAR, are proposed.
本数据挖掘系统使用增量算法发现关联规则和提取决策规则。
The data mining system can discover association rules and decision rules.
通过对挖掘关联规则增量更新中FUP算法的关键思想以及性能进行了研究，提出了改进的FUP算法SFUP。
An improved incremental updating algorithm SFUP is developed based on study of the principle and efficiency of FUP algorithm.
通过对挖掘关联规则增量更新中FUP算法的关键思想以及性能进行了研究，提出了改进的FUP算法SFUP。
An improved incremental updating algorithm SFUP is developed based on study of the principle and efficiency of FUP algorithm.
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