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Erschienen in:

2015 | OriginalPaper | Buchkapitel

A Graph-Based Concept Discovery Method for n-Ary Relations

verfasst von : Nazmiye Ceren Abay, Alev Mutlu, Pinar Karagoz

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

Concept discovery is a multi-relational data mining task for inducing definitions of a specific relation in terms of other relations in the data set. Such learning tasks usually have to deal with large search spaces and hence have efficiency and scalability issues. In this paper, we present a hybrid approach that combines association rule mining methods and graph-based approaches to cope with these issues. The proposed method inputs the data in relational format, converts it into a graph representation, and traverses the graph to find the concept descriptors. Graph traversal and pruning are guided based on association rule mining techniques. The proposed method distinguishes from the state-of-the art methods as it can work on n-ary relations, it uses path finding queries to extract concepts and can handle numeric values. Experimental results show that the method is superior to the state-of-the art methods in terms of accuracy and the coverage of the induced concept descriptors and the running time.

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Vorheriges Kapitel Implementation of Multidimensional Databases with Document-Oriented NoSQL

Nächstes Kapitel Exact Detection of Information Leakage in Database Access Control

http://neo4j.com.

Although the running time of the proposed method and that of CRIS are not directly comparable, as the experiments are conducted on different computers, we provide them to provide intuition. In addition, these results are obtained from a recent study hence the configurations of computers are expected to be similar.

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Titel: A Graph-Based Concept Discovery Method for n-Ary Relations
verfasst von: Nazmiye Ceren Abay
Alev Mutlu
Pinar Karagoz
Verlag: Springer International Publishing
Buch: Big Data Analytics and Knowledge Discovery
Print ISBN: 978-3-319-22728-3

Electronic ISBN: 978-3-319-22729-0

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-22729-0_30

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