Title :
Extracting symbolic objects from relational databases
Author :
Stéphan, Véronique
Author_Institution :
Inst. Nat. de Recherche en Inf. et Autom., Le Chesnay, France
Abstract :
Our aim is to define operators to retrieve groups of individuals from a relational database. The way we describe these groups makes it possible to analyse them by symbolic data analysis methods which extend classical ones to more complex data. In so far as the input consists of groups of data extensionally defined in the database, our problem is to find the best description representing each group in the formalism (called symbolic object) of symbolic data analysis. In our process, we take into account data from tables together with additional knowledge such as taxonomies. To describe each group, we perform a generalization step and a specialization one. Final descriptions are based on the notion of homogeneity within a group and they minimize a volume criterion
Keywords :
data analysis; data description; database theory; generalisation (artificial intelligence); knowledge acquisition; query processing; relational databases; complex data; data description; data mining; generalization; group homogeneity; operators; relational databases; specialization; symbolic data analysis; symbolic object extraction; tables; taxonomies; volume criterion; Active appearance model; Data analysis; Data mining; Information analysis; Relational databases; Sampling methods; Statistical analysis; Taxonomy;
Conference_Titel :
Database and Expert Systems Applications, 1996. Proceedings., Seventh International Workshop on
Conference_Location :
Zurich
Print_ISBN :
0-8186-7662-0
DOI :
10.1109/DEXA.1996.558606