Supervised Learning for Automatic Classification of Documents using Self-Organizing Maps

Dina Goren-Bar, Tsvi Kuflik, Dror Lev
2000 DELOS Workshops / Conferences  
Automatic Document Classification that corresponds with user-predefined classes is a challenging and widely researched area. Self-Organizing Maps (SOM) are unsupervised Artificial Neural Networks (ANN) which are mathematically characterized by transforming high-dimensional data into two-dimension representation, enabling automatic clustering of the input, while preserving higher order topology. A closely related algorithm is the Learning Vector Quantization (LVQ), which uses supervised learning
more » ... to maximize correct data classification. This study presents the application of SOM and LVQ to automatic document classification, based on predefined set of clusters. A set of documents, manually clustered by domain expert was used. Experimental results show considerable success of automatic document clustering that matches manual clustering, with a slight preference for the LVQ.
dblp:conf/delos/Goren-BarKL00 fatcat:65g6nhxjsrbczlom6wscv2z6ni