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...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,Or?owska’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.展开更多
Information tables having continuous domains are handled by neighborhood rough sets.Two approximations in complete information tables are extended to handle incomplete information.Consequently,four approximations are ...Information tables having continuous domains are handled by neighborhood rough sets.Two approximations in complete information tables are extended to handle incomplete information.Consequently,four approximations are obtained:certain and possible lower ones and certain and possible upper ones without computational complexity.These extended approximations create the same results as the ones from possible world semantics by using possible indiscernibility relations.Therefore,the extension is justified.In complete information tables two types of single rules that an object supports are obtained:consistent and inconsistent ones.The single rule has low applicability.To increase applicability,a series of single rules are brought into one combined rule with an interval value.In incomplete information tables four kinds of single rules are obtained.From them,four kinds of combined rules are obtained:certain and consistent,possible and consistent,certain and inconsistent,and possible inconsistent ones.A combined rule has higher applicability than the single rules from which it is assembled.展开更多
A rough set,first described by Polish computer scientist Zdzis?aw Pawlak,is a formal approximation of a crisp set,and it is now known as a new mathematical tool to process vague concepts.They are used for machine lear...A rough set,first described by Polish computer scientist Zdzis?aw Pawlak,is a formal approximation of a crisp set,and it is now known as a new mathematical tool to process vague concepts.They are used for machine learning,knowledge discovery,feature selection,etc.,and are applied to artificial intelligence,medical informatics,civil engineering,Kansei engineering,decision science,business administration,and so on.Especially,research on data mining using rough sets is widely spreading,and the obtained association rules are applied to the characterisation of data and decision support.展开更多
文摘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,Or?owska’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.
文摘Information tables having continuous domains are handled by neighborhood rough sets.Two approximations in complete information tables are extended to handle incomplete information.Consequently,four approximations are obtained:certain and possible lower ones and certain and possible upper ones without computational complexity.These extended approximations create the same results as the ones from possible world semantics by using possible indiscernibility relations.Therefore,the extension is justified.In complete information tables two types of single rules that an object supports are obtained:consistent and inconsistent ones.The single rule has low applicability.To increase applicability,a series of single rules are brought into one combined rule with an interval value.In incomplete information tables four kinds of single rules are obtained.From them,four kinds of combined rules are obtained:certain and consistent,possible and consistent,certain and inconsistent,and possible inconsistent ones.A combined rule has higher applicability than the single rules from which it is assembled.
文摘A rough set,first described by Polish computer scientist Zdzis?aw Pawlak,is a formal approximation of a crisp set,and it is now known as a new mathematical tool to process vague concepts.They are used for machine learning,knowledge discovery,feature selection,etc.,and are applied to artificial intelligence,medical informatics,civil engineering,Kansei engineering,decision science,business administration,and so on.Especially,research on data mining using rough sets is widely spreading,and the obtained association rules are applied to the characterisation of data and decision support.
