基于改进SPRINT分类算法的数据挖掘模型

Data mining model based on improved SPRINT classification algorithm

为解决目前数据挖掘模型分类时间长、挖掘准确率不高的问题,提出基于改进决策树分类算法(SPRINT)的数据挖掘模型。先采用最大-最小规范化公式完成原始数据线性变换,利用改进后的SPRINT分类算法按照输入数据特性进行分类,使用协同过滤技术生成与数据相近的属性集,计算数据属性相似度,生成语义规则集,为用户提供更优的数据服务。选取某公司营销数据集作为样本进行对比实验,结果表明,与对比模型相比,所提出的数据挖掘模型分类时间更短,挖掘准确率更高,能为用户提供更优质的数据服务。

In order to solve the problems of long classification time and low mining accuracy of current data mining models,a data mining model based on improved decision tree classification algorithm(SPRINT)is proposed.Firstly,the maximum-minimum normalization formula is used to complete the linear transformation of the original data,and the improved SPRINT classification algorithm is used to classify the input data according to the characteristics of the input data.The collaborative filtering technology is used to generate the attribute set similar to data,and calculate data attribute similarity to generate semantic rule set,which could provide users with better data services.A company’s marketing data set is selected as a sample for comparative experiments.The results show that,compared with the comparative model,the proposed data mining model has shorter classification time and higher mining accuracy,which can provide users with better data services.