Pregled bibliografske jedinice broj: 364615
Comparing Document Classification Schemes Using K-Means Clustering
Comparing Document Classification Schemes Using K-Means Clustering // Lecture Notes in Artificial Intelligence, 5177 (2008), 1; 615-624 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 364615 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
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
Projekti:
036-1300646-1986 - Otkrivanje znanja u tekstnim podacima (Dalbelo-Bašić, Bojana, MZO ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus
