Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
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A data mining component is included in Microsoft SQL Server 2000 and SQL Server 2005, one of the most popular DBMSs. This gives a push for data mining technologies to move from a niche towards the mainstream. Apart from a few algorithms, the main contribution of SQL Server Data Mining is the implementation of OLE DB for Data Mining. OLE DB for Data mining is an industrial standard led by Microsoft and supported by a number of ISVs. It leverages two existing relational technologies: SQL and OLE DB. It defines a SQL language for data mining based on a relational concept. More recently, Microsoft, Hyperion, SAS and a few other BI vendors formed the XML for Analysis Council. XML for Analysis covers both OLAP and Data Mining. The goal is to allow consumer applications to query various BI packages from different platforms. This paper gives an overview of OLE DB for Data Mining and XML for Analysis. It also shows how to build data mining application using these APIs.