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Pregled bibliografske jedinice broj: 915922

InterSet: Interactive Redescription Set Exploration


Mihelčić, Matej; Šmuc, Tomislav
InterSet: Interactive Redescription Set Exploration // Discovery Science 19th International Conference, DS 2016, Bari, Italy, October 19– 21, 2016, Proceedings / Calders, Toon ; Ceci, Michelangelo ; Malerba, Donato (ur.).
Bari, Italija: Springer International Publishing, 2016. str. 35-50 doi:10.1007/978-3-319-46307-0_3 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
InterSet: Interactive Redescription Set Exploration

Autori
Mihelčić, Matej ; Šmuc, Tomislav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Discovery Science 19th International Conference, DS 2016, Bari, Italy, October 19– 21, 2016, Proceedings / Calders, Toon ; Ceci, Michelangelo ; Malerba, Donato - : Springer International Publishing, 2016, 35-50

Skup
Discovery Science

Mjesto i datum
Bari, Italija, 19-21-10-2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Knowledge discovery Redescription mining Redescription set Interactive exploration ; Self organising map ; Heatmap ; Crossfilter

Sažetak
We propose a novel approach for interactive redescription set exploration and redescription analysis realized through the tool InterSet. The tool is developed for interaction with possibly large redescription sets, produced on large datasets, and it enables better understanding of the underlying data and relations between attribute sets. New insights from redescription sets can be obtained through three different interaction modes based on: (i) similarity of entity occurrence in redescription support sets, (ii) attribute co- occurence in redescriptions and (iii) redescription quality measures. These modes provide additional contextualization, which is a major advantage compared to current state of the art approaches that allow interactive redescription set exploration, enabling users to obtain new knowledge in the form of interesting redescription subsets which can be analysed further on the level of individual redescriptions.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2013-11-9623 - Postupci strojnog učenja za dubinsku analizu složenih struktura podataka (Dragan Gamberger, )

Ustanove
Institut "Ruđer Bošković", Zagreb

Časopis indeksira:


  • Scopus


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