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Comparing Document Classification Schemes Using K-Means Clustering (CROSBI ID 144558)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Šilić, Artur ; Moens, Marie-Francine ; Žmak, Lovro ; Dalbelo Bašić, Bojana Comparing Document Classification Schemes Using K-Means Clustering // Lecture notes in computer science, 5177 (2008), 1; 615-624

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

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Podaci o izdanju

5177 (1)

2008.

615-624

objavljeno

0302-9743

Povezanost rada

Računarstvo

Indeksiranost