Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination
Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination
复制标题
DOI：
10.1049/trit.2019.0001
复制
发表时间：
2019-12-01
影响因子：
5.1
通讯作者：
Nakata, Michinori
中科院分区：
文献类型：
--
作者：
Sakai, Hiroshi;Nakata, Michinori
作者一直在研究粗糙集、信息不完全、数据挖掘、粒计算等新的计算方法，开发了一些关于关联规则的软件工具和新的数学框架。他们将这项研究简称为粗糙集非确定性信息分析(RNIA)。他们遵循了几种新的研究类型，特别是Pawlak的粗糙集，Lipski的不完全信息数据库，Orowska的非确定性信息系统，Agrawal的Apriori算法。这些都是与信息不完全、数据挖掘和规则生成相关的杰出研究。他们一直在努力结合这些新颖的研究，并一直在努力实现更智能的规则生成器，以处理具有信息不完备性的数据集。本研究综述了作者在规则生成器方面的研究重点，并考虑了它们的组合。
The authors have been coping with new computational methodologies such as rough sets, information incompleteness, data mining, granular computing, etc., and developed some software tools on association rules as well as new mathematical frameworks. They simply term this research Rough sets Non-deterministic Information Analysis (RNIA). They followed several novel types of research, especially Pawlak's rough sets, Lipski's incomplete information databases, Ortowska's non-deterministic information systems, Agrawal's Apriori algorithm. These are outstanding researches related to information incompleteness, data mining, and rule generation. They have been trying to combine such novel researches, and they have been trying to realise more intelligent rule generator handling data sets with information incompleteness. This study surveys the authors' research highlights on rule generators, and considers a combination of them.