Detail publikace

Using Standard APIs for Data Mining in Prediction

Originální název

Using Standard APIs for Data Mining in Prediction

Anglický název

Using Standard APIs for Data Mining in Prediction

Jazyk

en

Originální abstrakt

The chapter presents a simple example in which three standard application programming interfaces (APIs) for data mining - OLEDB for DM, SQL/MM DM a JDM - are used in prediction. Namely, based on a debt level, income level and employment type we want to predict the credit risk of a customer.

The standards are usually presented separately. The fact that the same data mining task is solved by means of three different standards can help the reader in understanding differences among the standards and in getting the picture of complexity of their use.

Anglický abstrakt

The chapter presents a simple example in which three standard application programming interfaces (APIs) for data mining - OLEDB for DM, SQL/MM DM a JDM - are used in prediction. Namely, based on a debt level, income level and employment type we want to predict the credit risk of a customer.

The standards are usually presented separately. The fact that the same data mining task is solved by means of three different standards can help the reader in understanding differences among the standards and in getting the picture of complexity of their use.

BibTex


@inbook{BUT55599,
  author="Jaroslav {Zendulka}",
  title="Using Standard APIs for Data Mining in Prediction",
  annote="The chapter presents a simple example in which three standard application programming interfaces (APIs) for data mining - OLEDB for DM, SQL/MM DM a JDM - are used in prediction. Namely, based on a debt level, income level and employment type we want to predict the credit risk of a customer. 

The standards are usually presented separately. The fact that the same data mining task is solved by means of three different standards can help the reader in understanding differences among the standards and in getting the picture of complexity of their use.", address="Idea Group Publishing", booktitle="Encyclopedia of Data Warehousing and Mining. Volume 2.", chapter="55599", institution="Idea Group Publishing", year="2005", month="july", pages="1171--1174", publisher="Idea Group Publishing", type="book chapter" }