基于人工免疫系统的关联规则增量挖掘

Incremental Updating Algorithm Based on Artificial Immune System for Mining Association Rules

本文采用人工免疫算法进行关联规则挖掘,通过权值设置发现在事务数据集中有意义的二进制关系,将挖掘工作集中在那些有着特殊权值的有意义的关联项,避免了挖掘工作在大量的无意义的关系项中搜索。实验证明,此算法是有效的且灵活性强,能在Web使用数据集中发现有意义的带权值的关联规则。同时给出了在最小支持度和最小置信度不变的情况下,在动态数据集中进行增量关联规则挖掘的方法。同样使用权值方法来提升新数据集的重要性。此方法的可行性和有效性同样在实验中体现出来。

We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight.The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatorial explosion of insignificant relationships.A new algorithm is developed based on artificial immune system and on the improved model for association rules mining.The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on web usage datasets.Meanwhile, we also propose a strategy for maintaining association rules in dynamic databases.We assume that the two thresholds,min support and min confidence,do not change.This method uses weighting technique to highlight new data.The experiments have shown that our approach is efficient and promising.