Publication detail

Using Standard APIs for Data Mining in Prediction

ZENDULKA, J.

Original Title

Using Standard APIs for Data Mining in Prediction

English Title

Using Standard APIs for Data Mining in Prediction

Type

book chapter

Language

en

Original Abstract

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.

English abstract

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.

Keywords

data mining, OLEDB for DM, SQL/MM DM, JDM, logical data, physical data, prediction join

RIV year

2005

Released

08.07.2005

Publisher

Idea Group Publishing

Location

Hershey

ISBN

1-59140-557-2

Book

Encyclopedia of Data Warehousing and Mining. Volume 2.

Pages from

1171

Pages to

1174

Pages count

4

Documents

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" }