Comparing Document Classification Schemes Using K-Means Clustering (CROSBI ID 144558)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šilić, Artur ; Moens, Marie-Francine ; Žmak, Lovro ; Dalbelo Bašić, Bojana
engleski
Comparing Document Classification Schemes Using K-Means Clustering
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.
K-means; clustering; classification scheme; cluster visualization; PCA
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano