Li, Zhenpeng

Shang, Changjing

Shen, Qiang

English

Identification of hidden relationships between domain attributes from different data sources is of great practical significance and forms an emerging field in data mining. However, currently there seldom exist any systematic methods that can effectively handle this problem, especially when dealing with imprecisely described associations. In this paper, a novel data-driven approach for inter-variable correlation prediction is proposed by exploiting the concept of connected-triples. The work is implemented with the use of fuzzy logic. Through the exploitation of link strength measurements and fuzzy inference, the job of detecting similar or related variables can be accomplished via examining link relation patterns within and across different data sources. Empirical evaluation results are discussed, revealing the potential of the proposed work in predicting interesting attribute relations, while involving simple computation mechanism

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Inter-variable correlation prediction with fuzzy connected-triples

http://pure.aber.ac.uk/ws/files/27202426/Li2018_Article_Inter_variableCorrelationPredi.pdf

Inter-variable correlation prediction with fuzzy connected-triples

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