Traditional databases store large collections of information in the form of structured records, and provide methods for querying the database to obtain all records whose
content satisfies the user's query. More recently, however, researchers in Knowledge Discovery in Databases (KDD) have provided a new family of tools for accessing information in databases (e.g. Brachman etal, 1993; Frawley et al, 1991; Kloesgen,
1992; Kloesgen, 1995b; Ezawa and Norton, 1995). The goal of KDD has been defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful
information from given data" (Piatetsky- Shapiro and Frawley 1991). Work in this area includes applying machine-learning and statistical-analysis techniques towards the automatic discovery of patterns in databases, as well as providing user-guided environments for exploration of data.
content satisfies the user's query. More recently, however, researchers in Knowledge Discovery in Databases (KDD) have provided a new family of tools for accessing information in databases (e.g. Brachman etal, 1993; Frawley et al, 1991; Kloesgen,
1992; Kloesgen, 1995b; Ezawa and Norton, 1995). The goal of KDD has been defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful
information from given data" (Piatetsky- Shapiro and Frawley 1991). Work in this area includes applying machine-learning and statistical-analysis techniques towards the automatic discovery of patterns in databases, as well as providing user-guided environments for exploration of data.
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