Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
KOHONEN NETWORKS WITH GRAPH-BASED AUGMENTED METRICS Peter Andras and Olusola Idowu
 

Summary: KOHONEN NETWORKS WITH GRAPH-BASED AUGMENTED METRICS
Peter Andras and Olusola Idowu
School of Computing Science
University of Newcastle
Newcastle upon Tyne, NE1 7RU, UK
{peter.andras,o.c.idowu}@ncl.ac.uk
Abstract Correct and efficient text classification is a major challenge in today's world of
rapidly increasing amount of accessible electronic text data. Kohonen networks have been
applied to document classification with comparable success to other document clustering
methods. An important challenge is to devise text similarity metrics that can improve the
performance of text classification Kohonen networks by integrating more semantic information
into the metric. Here we propose an augmented metric for text similarity that is based on the
comparison of word consecutiveness graphs of documents. We show that using the proposed
augmented similarity metric Kohonen networks perform better than Kohonen networks using
usual Euclidean distance metric comparison of word frequency vectors. Our results indicate
that word consecutiveness graph comparison includes more semantic information into the text
similarity measure improving text classification performance.
Key words augmented metric, Kohonen network, text classification, word consecutiveness
graph
1 Introduction

  

Source: Andras, Peter - School of Computing Science, University of Newcastle upon Tyne

 

Collections: Computer Technologies and Information Sciences