The discovery of emerging patterns (EPs) is a descriptive data mining task defined for pre-classified data. It aims at detecting patterns which contrast two classes and has been extensively investigated for attribute-value representations. In this work we propose a method, named Mr-EP, which discovers EPs from data scattered in multiple tables of a relational database. Generated EPs can capture the differences between objects of two classes which involve properties possibly spanned in separate data tables. We implemented Mr-EP in a pre-existing multi-relational data mining system which is tightly integrated with a relational DBMS, and then we tested it on two sets of geo-referenced data.
Discovering Relational Emerging Patterns
APPICE, ANNALISA;CECI, MICHELANGELO;MALERBA, Donato
2007-01-01
Abstract
The discovery of emerging patterns (EPs) is a descriptive data mining task defined for pre-classified data. It aims at detecting patterns which contrast two classes and has been extensively investigated for attribute-value representations. In this work we propose a method, named Mr-EP, which discovers EPs from data scattered in multiple tables of a relational database. Generated EPs can capture the differences between objects of two classes which involve properties possibly spanned in separate data tables. We implemented Mr-EP in a pre-existing multi-relational data mining system which is tightly integrated with a relational DBMS, and then we tested it on two sets of geo-referenced data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
