A Framework for Object-Oriented Data Mining Based on Higher-Order Logic Programming

Data mining discovers knowledge and useful information from large amounts of data stored in databases. With the increasing popularity of object-oriented database system in advanced database applications, it is significantly important to study the data mining methods for object-oriented database. Thi...

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Vydané v:Applied Mechanics and Materials Ročník 420; číslo Recent Trends in Materials and Mechanical Engineering II; s. 325 - 332
Hlavní autori: Zhang, Zhi Ping, Wang, Li Jun, Yu, Hai Yan, Li, Lin Na
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Zurich Trans Tech Publications Ltd 01.09.2013
Predmet:
Data mining
Formalism
Languages
Logic programming
Object oriented
Object-oriented programming
Representations
Data Mining (DM)
Higher-Order Logic Programming
Object-Oriented Database
ISBN:3037858699, 9783037858691
ISSN:1660-9336, 1662-7482, 1662-7482
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Popis
Shrnutí:Data mining discovers knowledge and useful information from large amounts of data stored in databases. With the increasing popularity of object-oriented database system in advanced database applications, it is significantly important to study the data mining methods for object-oriented database. This paper proposes that higher-order logic programming languages and techniques is very suitable for object-oriented data mining, and presents a framework for object-oriented data mining based on higher-order logic programming. Such a framework is inductive logic programming which adopts higher-order logic programming language Escher as knowledge representation formalism. In addition, Escher is a generalization of the attribute-value representation, thus many higher-order logic learners under this framework can be upgraded directly from corresponding propositional learners.
Bibliografia:Selected, peer reviewed papers from the 2013 2nd International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2013), September 21-23, 2013, Singapore
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ISBN:3037858699
9783037858691
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.420.325