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Pregled bibliografske jedinice broj: 984377

Extending Redescription Mining to Multiple Views


Mihelčić, Matej; Džeroski, Sašo; Šmuc, Tomislav
Extending Redescription Mining to Multiple Views // 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / Soldatova, Larisa ; Vanschoren, Joaquin ; Papadopoulos, George ; Ceci, Michelangelo (ur.).
Cham: Springer International Publishing, 2018. str. 292-307 doi:10.1007/978-3-030-01771-2_19


Naslov
Extending Redescription Mining to Multiple Views

Autori
Mihelčić, Matej ; Džeroski, Sašo ; Šmuc, Tomislav

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings

Urednik/ci
Soldatova, Larisa ; Vanschoren, Joaquin ; Papadopoulos, George ; Ceci, Michelangelo

Izdavač
Springer International Publishing

Grad
Cham

Godina
2018

Raspon stranica
292-307

ISBN
978-3-030-01771-2

Ključne riječi
Redescription mining CLUS-RM ; Predictive Clustering trees ; World countries ; River quality

Sažetak
Redescription mining is a data mining task that discovers re-descriptions of different subsets of entities from available data. Locating such re-descriptions is important in many scientific disciplines because it allows detecting different types of associations including synergy of different attributes of interest. There exist a number of redescription mining algorithms, however they are all restricted to use of one or maximally two disjoint sets of attributes (views) to re-describe different subsets of entities. The main reasons for this limitation are computational complexity and potentially large increase in number of produced patterns, in multi-view setting, during redescription mining. In this work we present an algorithm that allows mining redescriptions from multiple views using the CLUS-RM algorithm. Presented algorithm efficiently solves aforementioned problems. Its computational complexity, with respect to attribute operations, increases linearly with the increase of number of views and we present techniques to handle large number of produced redescriptions during redescription mining step.

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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