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Relational Data Mining pp 339–364Cite as

Relational Data Mining Applications: An Overview

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Abstract

This chapter gives an overview of applications of relational learning and inductive logic programming to data mining problems in a variety of areas. These include bioinformatics, where successful applications come from drug design, predicting mutagenicity and carcinogenicity, and predicting protein structure and function, including genome scale prediction of protein functional class. Other application areas include medicine, environmental sciences and monitoring, mechanical and traffic engineering. Applications of relational learning are also emerging in business data analysis, text and Web mining, and miscellaneous other fields, such as the analysis of musical performances.

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Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia

    Sašo Džeroski

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  1. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Sašo Džeroski  & Nada Lavrač  & 

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Džeroski, S. (2001). Relational Data Mining Applications: An Overview. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_14

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  • DOI: https://doi.org/10.1007/978-3-662-04599-2_14

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