基于中智模糊关联规则生成的大数据挖掘分析算法

关联规则挖掘作为一种大数据挖掘方法,被用于确定不同项目间存在的内在联系,并以大于某一阈值作为评判不同项间存在关联的依据。传统关联规则挖掘方法仅能建立不同项间的布尔型关联规则,存在硬化数据“尖锐边界”问题导致关联规则挖掘性能下降的缺点。为克服经典数据挖掘算法的这一缺陷,提出一种新型中智关联规则挖掘算法。基于专家知识对语言学术语进行量化预处理,得到不同指标的量化数据资料库;基于关联规则支持度定义计算不同指标项集合的支持度;通过考虑不同项间的隶属度、不确定度和非隶属度函数生成关联规则。将该中智挖掘算法与模糊挖掘算法进行对比,结果表明,该算法能够增加生成关联规则数量,有助于提高数据挖掘的准确性。

BIG DATA MINING AND ANALYSIS ALGORITHM BASED ON NEUTROSOPHIC FUZZY ASSOCIATION RULE GENERATION

Association rule mining,as a big data mining method,is used to determine the intrinsic relationship between different items,and we use a threshold greater than a certain value as the basis for judging the correlation between different items.The traditional Boolean association rule mining method can only generate the binary rules among different items.However,the problem of hardening data “sharp boundary” leads to the performance degradation of association rules mining.To overcome this drawback,we proposed a neutrosophic association rule mining algorithm.Based on expert knowledge,the linguistic terms were preprocessed into the quantitative values to obtain quantitative databases of different indicators.The support degree of different index item sets was calculated based on the definition of association rule support degree.Furthermore,the association rules were generated by considering the membership,indeterminacy,and non-membership functions of different items.We compared the proposed algorithm with the fuzzy mining algorithm.The results show that our proposed can increase the number of generated association rules and help to improve the accuracy of data mining.