A Survey on Relational Database Based Multi Relational Classification Algorithms

Komal Shah, Kajal S Patel
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Abstract

Classification on real world database is an important task in data mining. Many classification algorithms can build model only for data in single flat file as input, whereas most of real-world data bases are stored in multiple tables and managed by relational database systems. As conversion of relational data from multiple tables into a single flat file usually causes many problems, development of multi relational classification algorithms becomes popular area of research interests. Relational database based multi relational classification algorithms aim to build a model that can predict class label of unknown tuple with the help of background table knowledge.  This method keeps database in it normalized form without distorting structure of database. This paper presents survey of existing multi relational classification algorithms based on relational database.
基于关系数据库的多关系分类算法概览
对现实世界的数据库进行分类是数据挖掘的一项重要任务。许多分类算法只能为作为输入的单个平面文件中的数据建立模型,而现实世界中的大多数数据库都存储在多个表中,并由关系数据库系统管理。由于将多个表中的关系数据转换为单一平面文件通常会引起许多问题,因此开发多关系分类算法成为研究兴趣的热门领域。基于关系数据库的多关系分类算法旨在建立一个模型,借助背景表知识预测未知元组的类标签。 这种方法保持了数据库的规范化形式,不会扭曲数据库的结构。本文介绍了现有的基于关系数据库的多关系分类算法。
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