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Pregled bibliografske jedinice broj: 984371

Targeted and contextual redescription set exploration


Mihelčić, Matej; Šmuc, Tomislav
Targeted and contextual redescription set exploration // Machine Learning, 107 (2018), 11; 1809-1846 doi:10.1007/s10994-018-5738-9 (međunarodna recenzija, članak, znanstveni)


Naslov
Targeted and contextual redescription set exploration

Autori
Mihelčić, Matej ; Šmuc, Tomislav

Izvornik
Machine Learning (0885-6125) 107 (2018), 11; 1809-1846

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
One important problem occurring in redescription mining is a very large number of produced redescriptions. This makes analyses time consuming and generally difficult. We present the targeted and contextual redescription set exploration, realized through the tool InterSet. The main purpose of the tool is to derive additional knowledge from the redescription set which allows exploring parts of redescription set of interest and examining redescriptions individually or in the broader context, with the aim of increasing overall understandability. InterSet allows relating, grouping redescriptions, observing distributions of various redescription properties and selecting the appropriate subsets for further, detailed study. This allows gaining knowledge about the underlying data, help in forming, understanding, supporting research hypothesis or assists in understanding one or more redescriptions of interest. The tool provides three different, fully connected interaction modes based on: (1) similarity of entity occurrence in redescription support sets, (2) attribute co- occurrence in redescriptions and (3) redescription quality measures. Additionally, it allows exploration of relations between different redescriptions by creating a graph visualization that includes the top k-shortest paths containing selected redescriptions. On the individual redescription level, it allows studying value distributions of described entities, for a given set of attributes.

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:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
  • Scopus


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