目前数据库关联规则的增量挖掘作为数据挖掘的一个重要的领域，已经广泛应用于教育，医疗，卫生等领域，因此它成为了当今数据挖掘中最活跃，最重要的一个分支领域。数据库中的数据存在大量未知的数据以及不可知的数据变化。若采用Apriori算法进行计算，一方面很难取得较好的结果，另一方面支持度的变化对结果的影响很大，无法确定支持度的变化，因此借助属性论中定性属性的机理以及属性计算网络的边界学习算法，结合IUBM 算法提出了一种基于定性属性的关联规则的增量挖掘算法。比如在以分数划线招生制度下，定性基准的一分之差，可能完全改变一个学生的一生的命运。通过实验表明，该算法在处理大规模数据的增量式关联规则的挖掘中减少了冗余规则的产生，同时挖掘效率得到了很大的提升。对于诸如预测大学生就业的情况及招聘企业对于应届生学习情况的了解等应用十分有意义。%Nowadays, the present incremental database association rule mining is an important area as data mining, has been widely used in education, medical, health and other fields, so it has become the most active data min are change and unknown. If use the Apriori algorithm to calculate, on the one hand it is difficult to achieve good results, on the other hand, a great impact on support changes we can not determine the support change. So with the mechanism of qualitative attribute theory and attribute computing network boundary learning algorithm with IUBM algorithm, we propose an algorithm for mining association rules based on incremental qualitative attribute. For example, in order to score crossed the enrollment system, qualitative datum point, it may completely change the life of a student’s life. With the experiments show that, this algorithm reduces the redundant rules generated in the incremental mining association rules in large-scale data processing, at the same time the mining efficiency has been greatly improved. Apply the researches to prediction of College Students’ employment for graduates to understand the learning situation of application is very meaningful.
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