Ghent University Academic Bibliography

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SIAS-miner : mining subjectively interesting attributed subgraphs

Anes Bendimerad, Ahmad Mel (UGent) , Jefrey Lijffijt (UGent) , Marc Plantevit, Celine Robardet and Tijl De Bie (UGent)

(2020) DATA MINING AND KNOWLEDGE DISCOVERY. 34. p.355-393
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Interactive visual data exploration with subjective feedback : an information-theoretic approach

Kai Puolamäki, Emilia Oikarinen, Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020) DATA MINING AND KNOWLEDGE DISCOVERY. 34(1). p.21-49
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Relaxing the strong triadic closure problem for edge strength inference

Florian Adriaens (UGent) , Tijl De Bie (UGent) , Aristides Gionis, Jefrey Lijffijt (UGent) , Antonis Matakos and Polina Rozenshtein

(2020) DATA MINING AND KNOWLEDGE DISCOVERY. 34. p.611-651
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Mining explainable local and global subgraph patterns with surprising densities

Junning Deng (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020) DATA MINING AND KNOWLEDGE DISCOVERY.
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Subjectively interesting connecting trees and forests

Florian Adriaens (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2019) DATA MINING AND KNOWLEDGE DISCOVERY. 33(4). p.1088-1124
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Multi-target prediction : a unifying view on problems and methods

Willem Waegeman (UGent) , Krzysztof Dembczyński and Eyke Hüllermeier

(2019) DATA MINING AND KNOWLEDGE DISCOVERY. 33(2). p.293-324
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SICA : subjectively interesting component analysis

Bo Kang (UGent) , Jefrey Lijffijt (UGent) , Raúl Santos-Rodríguez and Tijl De Bie (UGent)

(2018) DATA MINING AND KNOWLEDGE DISCOVERY. 32(4). p.949-987
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Exceptional model mining: supervised descriptive local pattern mining with complex target concepts

Wouter Duivesteijn (UGent) , Ad J. Feelders and Arno Knobbe

(2016) DATA MINING AND KNOWLEDGE DISCOVERY. 30(1). p.47-98
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Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Michiel Stock (UGent) , Krzysztof Dembczynski, Bernard De Baets (UGent) and Willem Waegeman (UGent)

(2016) DATA MINING AND KNOWLEDGE DISCOVERY. 30(5). p.1370-1394
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Size matters: choosing the most informative set of window lengths for mining patterns in event sequences

Jefrey Lijffijt (UGent) , Panagiotis Papapetrou and Kai Puolamaki

(2015) DATA MINING AND KNOWLEDGE DISCOVERY. 29(6). p.1838-1864

Academic Bibliography

SIAS-miner : mining subjectively interesting attributed subgraphs

Interactive visual data exploration with subjective feedback : an information-theoretic approach

Relaxing the strong triadic closure problem for edge strength inference

Mining explainable local and global subgraph patterns with surprising densities

Subjectively interesting connecting trees and forests

Multi-target prediction : a unifying view on problems and methods

SICA : subjectively interesting component analysis

Exceptional model mining: supervised descriptive local pattern mining with complex target concepts

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Size matters: choosing the most informative set of window lengths for mining patterns in event sequences

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