基于数据场的量化关联规则挖掘方法设计

Design of Quantitative Association Rules Mining Method Based on Data Field

目前关联规则挖掘多集中在布尔型关联规则的挖掘,对量化关联规则的挖掘研究较少,传统方法多是将量化属性离散化,进而转化为布尔型关联规则的挖掘。为了克服传统方法中区间划分过硬问题,本文设计了基于数据场的量化关联规则挖掘方法,并使用数据场的场量定义支持度和置信度的计算公式。该方法充分考虑数据集中数据的非完备性以及各个数据对数据挖掘任务所发挥的不同作用,可使得挖掘得到的关联规则更精确。

At present, the association rule mining focuses on Boolean association rule mining, few on quantitative association rules mining. Traditional methods are used to discrete quantify attribute, and then transform quantitative association rule mining to Boolean association rule mining. In order to overcome the interval divided excellent problem of traditional method, this paper designs the mining method of quantitative attribute association rides based on data field, and defines the formula for calculating support and confidence based on the quantitative of data field. This method is fully considered the incomplete of data in dataset and the different roles of each data for data mining, and makes association rules by mining accurate.