Extending Redescription Mining to Multiple Views (CROSBI ID 63127)
Prilog u knjizi | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Mihelčić, Matej ; Džeroski, Sašo ; Šmuc, Tomislav
engleski
Extending Redescription Mining to Multiple Views
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.
Redescription mining CLUS-RM ; Predictive Clustering trees ; World countries ; River quality
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Podaci o prilogu
292-307.
objavljeno
10.1007/978-3-030-01771-2_19
Podaci o knjizi
21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings
Soldatova, Larisa ; Vanschoren, Joaquin ; Papadopoulos, George ; Ceci, Michelangelo
Cham: Springer
2018.
978-3-030-01771-2
0302-9743
1611-3349