均匀分布下不确定数据的关联规则变粒度查询  

Granularity transform query on association rules that mined from uniform distributed uncertain data

作　　者：陈爱东[1] 刘国华[1] 肖瑞[1] 万小妹[1] 石丹妮[1] 

机构地区：[1]东华大学计算机科学与技术学院,上海201600

出　　处：《计算机工程与科学》2013年第10期79-88,共10页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(61070032)

摘　　要：云计算为大数据的关联规则挖掘与查询提供了平台。为防止隐私泄漏,大数据中往往包含人为添加的不确定因素,如何使用户对不确定数据的关联规则挖掘结果查询透明化是大数据挖掘结果查询亟待解决的问题。在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布特性,这一特性不利于精确查询,但可为关联规则挖掘结果集的变粒度查询提供便利。首先,通过UFIDM算法进行挖掘并构建关联规则库,为提高查询效率,对泛化标识符和敏感属性分别构建Hilbert packed R树索引。在此基础上,提出了泛化值粒度转换方法和U-ARS查询算法。最后,通过理论分析和实验比对,展示了算法的可行性和有效性。Cloud computing provides the platform for the associate rule mining and query of big data.Data often contains artificially added uncertainty to prevent the information disclosure.How to allow users to query the result of association rules mining from uncertain data transparently is an urgent problem to be solved in the query of big data mining results.The uncertain big data for sharing achieves uniform distributed characteristic through generalizing precise data,this characteristic is not conductive to accurate queries but can offer convenience for the query on association rules mining result set.Firstly,the association rule library is built by UFI-DM algorithm and the R-tree indexes are constructed for both generalized identifiers and sensitive attributes separately in order to improve the query efficiency.Secondly,the generalization value granularity transform method and U-ARS query algorithm are proposed on this basis.Finally,theoretical analysis and experimental results demonstrate the feasibility and effectiveness of the algorithm.

关 键 词：大数据 均匀分布不确定数据 关联规则 变粒度查询 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]