http://www.cnr.it/ontology/cnr/individuo/prodotto/ID77612
Data Mining for Effective Risk Analysis in a Bank Intelligence Scenario. (Contributo in atti di convegno)
- Type
- Label
- Data Mining for Effective Risk Analysis in a Bank Intelligence Scenario. (Contributo in atti di convegno) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/ICDEW.2007.4401083 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Giovanni Costa; Francesco Folino; Antonio Locane; Giuseppe Manco; Riccardo Ortale (literal)
- Pagina inizio
- Pagina fine
- Note
- Google Scholar (literal)
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- DBLP (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto di calcolo e reti ad alte prestazioni; Istituto di calcolo e reti ad alte prestazioni; EXEURA; Istituto di calcolo e reti ad alte prestazioni; Istituto di calcolo e reti ad alte prestazioni (literal)
- Titolo
- Data Mining for Effective Risk Analysis in a Bank Intelligence Scenario. (literal)
- Abstract
- We propose a data warehousing architecture for effective risk analysis in a banking scenario. The core of the archi- tecture consists in two data mining tools for improving the quality of consolidated data during the acquisition process. Specifically, we deal with schema reconciliation, i.e. seg- mentation of a string sequence according to fixed attribute schema. To this purpose we present the RecBoost method- ology which pursuits effective reconciliation via multiple stages of classification. In addition, we propose a hash- based technique for data reconciliation, i.e. the recognition of apparently different records that, as a matter of fact, refer to the same real-world entity. (literal)
- Prodotto di
- Autore CNR
Incoming links:
- Prodotto
- Autore CNR di