Self-Organizing Maps
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Towards effective document clustering: A constrained K-means based approach
Information Processing and Management: an International Journal
Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
Knowledge-based vector space model for text clustering
Knowledge and Information Systems
On ontology-driven document clustering using core semantic features
Knowledge and Information Systems - Special Issue on "Context-Aware Data Mining (CADM)"
Text document clustering using global term context vectors
Knowledge and Information Systems
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Despite various advantages of traditional feature vector model for document representation, the well-known inherent deficiency in this model is "sovereign term assumption". This deficiency makes it impossible to identify syntactically different but semantically related terms. In this paper, we demonstrate the use of semantic similarity measure for quantifying the relationship between related terms. Identifying such relationships help in reducing the difference between related documents. In this work, we use only noun terms for enriching the representation model. The natural visualization of clusters is investigated in this study using Emergent Self Organizing Map (ESOM). Experimental results show that incorporation of semantic relationship enhances the accuracy of clustering results.