A Counting-Based Heuristic for ILP-Based Concept Discovery Systems

Multi-relational data mining (MRDM) is concerned with discovering hidden patterns from multiple tables in a relational database. One of the most commonly addressed tasks in MRDM is concept discovery in which the problem is inducing logical definitions of a specific relation, called target relation, in terms of other relations, called background knowledge. Inductive Logic Programming-based and graph-based approaches are two main competitors in this research. In this paper, we aim to introduce concept discovery problem and compare state-of-the-art methods in graph-based concept discovery by means of data representation, search method, and concept descriptor evaluation mechanism.

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Graph-Based Concept Discovery

Mutlu

Karagöz

Kavurucu

2018

Encyclopedia of Information Science and Technology, Fourth Edition

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Graph-Based Concept Discovery

Mutlu

Karagöz

Kavurucu

2019

Advances in Computer and Electrical Engineering

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Multi-relational data mining (MRDM) is concerned with discovering hidden patterns from multiple tables in a relational database. One of the most commonly addressed tasks in MRDM is concept discovery in which the problem is inducing logical definitions of a specific relation, called target relation, in terms of other relations, called background knowledge. Inductive logic programming-based and graph-based approaches are two main competitors in this research. In this chapter, the authors aim to introduce concept discovery problem and compare state-of-the-art methods in graph-based concept discovery by means of data representation, search method, and concept descriptor evaluation mechanism.

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A Counting-Based Heuristic for ILP-Based Concept Discovery Systems

Cited by 2 publications

References 12 publications

Graph-Based Concept Discovery

Graph-Based Concept Discovery

Graph-Based Concept Discovery

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