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Redescription Mining with Multi-target Predictive Clustering Trees


Mihelčić, Matej; Džeroski, Sašo; Lavrač, Nada; Šmuc, Tomislav
Redescription Mining with Multi-target Predictive Clustering Trees // New Frontiers in Mining Complex Patterns 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers
Porto, Portugal: Springer International Publishing, 2016. str. 125-143 doi:10.1007/978-3-319-39315-5_9 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Redescription Mining with Multi-target Predictive Clustering Trees

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

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

Izvornik
New Frontiers in Mining Complex Patterns 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers / - : Springer International Publishing, 2016, 125-143

Skup
New Frontiers in Mining Complex Patterns

Mjesto i datum
Porto, Portugal, 07.09.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Knowledge discovery ; Redescription mining ; Predictive clustering trees ; World countries

Sažetak
Redescription mining is a field of knowledge discovery that aims to find different descriptions of subsets of elements in the data by using two or more disjoint sets of descriptive attributes. The ability to find connections between different sets of descriptive attributes and provide a more comprehensive set of rules makes it very useful in practice. In this work, we introduce redescription mining algorithm for generating and iteratively improving a redescription set of user defined size based on multi-target Predictive Clustering Trees. This approach uses information about element membership in different generated rules to search for new redescriptions and is able to produce highly accurate, statistically significant redescriptions described by Boolean, nominal or numeric attributes. As opposed to current tree- based approaches that use multi-class or binary classification, we explore benefits of using multi target classification and regression to create redescriptions. The process of iterative redescription set improvement is illustrated on the dataset describing 199 world countries and their trading patterns. The performance of the algorithm is compared against the state of the art redescription mining algorithms.

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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