Exceptional Preferences Mining

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

de Sa, CR (Author) Other; Duivesteijn, W (Author) Other; Carlos Soares (Author) FEUP; Knobbe, A (Author) Other

Conference proceedings International

Title: DISCOVERY SCIENCE, (DS 2016)

Pages: 3-18

19th International Conference on Discovery Science (DS) / 27th International Conference on Algorithmic Learning Theory (ALT)

Bari, ITALY, OCT 19-21, 2016

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 14 Citations

Other information

Authenticus ID: P-00M-3X4

DOI: 10.1007/978-3-319-46307-0_1

Abstract (EN): Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes 'exceptional' varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16