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Comparing Document Classification Schemes Using K-Means Clustering


Šilić, Artur; Moens, Marie-Francine; Žmak, Lovro; Dalbelo Bašić, Bojana
Comparing Document Classification Schemes Using K-Means Clustering // Lecture Notes in Artificial Intelligence, 5177 (2008), 1; 615-624 (međunarodna recenzija, članak, znanstveni)


Naslov
Comparing Document Classification Schemes Using K-Means Clustering

Autori
Šilić, Artur ; Moens, Marie-Francine ; Žmak, Lovro ; Dalbelo Bašić, Bojana

Izvornik
Lecture Notes in Artificial Intelligence (0302-9743) 5177 (2008), 1; 615-624

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

Ključne riječi
K-means; clustering; classification scheme; cluster visualization; PCA

Sažetak
In this work, we jointly apply several text mining methods to a corpus of legal documents in order to compare the separation quality of two inherently different document classification schemes. The classification schemes are compared with the clusters produced by the K-means algorithm. In the future, we believe that our comparison method will be coupled with semi-supervised and active learning techniques. Also, this paper presents the idea of combining K-means and Principal Component Analysis for cluster visualization. The described idea allows calculations to be performed in reasonable amount of CPU time.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
036-1300646-1986 - Otkrivanje znanja u tekstnim podacima (Bojana Dalbelo-Bašić, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb