基于模糊运算的非结构化数据特征挖掘模型

Feature mining model of unstructured data based on fuzzy operation

为了解决非结构化数据所具备的数量巨大、模式滞后等弊端,提出一种基于模糊运算的非结构化数据特征挖掘模型方案。建立数据特征挖掘模型并挖掘特征,引入模糊运算、非结构化数据、特征空间、数据类型等多种空间概念,成功构建基于模糊运算的非结构化数据特征挖掘模型框架。设计了系统功能类库,包括数据类型库、特征类型库、数据特征类型关系库以及数据存储和处理引擎。并提出非结构化数据特征挖掘的模糊运算系统,发现在应用中无论是存储原始数据、特征数据,还是在特征空间、上传数据、检索查询、高级操作中,都能够灵活方便地实现非结构化数据处理,并且用户在应用基于模糊运算的非结构化数据特征挖掘模型时,也能够灵活化处理非结构数据特征。

In order to solve the problem of large quantity and lag pattern of unstructured data effectively,a feature mining model of unstructured data based on fuzzy operation is proposed.Establish a data feature mining model and mine features,introduces many spatial concepts such as fuzzy operation,unstructured data,feature space and data type,and successfully constructs the framework of unstructured data feature mining model based on fuzzy operation.The system function library is designed,including data type library,feature type library,data feature type relation library,data storage and processing engine.The fuzzy operation system of unstructured data feature mining is proposed.It is found that in the application,no matter in the storage of original data,feature data,or in the feature space,upload data,retrieval query,advanced operation,unstructured data processing can be realized flexibly and conveniently,and users can also use the unstructured data feature mining model based on fuzzy operation,characteristics of unstructured data processed by flexible activation.