Comparison of similarity measures for clustering Turkish documents

Ainura Madylova*, Şule Gündüz Öǧüdücü

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Text clustering has become an important part of the web data organization with the rapid growth of the World Wide Web (www). Clustering simplifies web search engine work by grouping large amount of documents, retrieved according to a given query. Similarity measures used in clustering affect the output of the grouping directly. Most of the document clustering techniques rely on single term analysis of text, such as vector space model. In order to improve grouping of Turkish documents, we investigate several similarity measures based on the semantic similarity of terms. Moreover, some techniques for calculating documents similarity are studied. The aim of this paper is to study the effects of semantic and single term similarity measures to the clustering results of Turkish documents. All experiments are carried out on Turkish web sites, taking into account the relationships of terms based on the ontology for the Turkish language.

Original languageEnglish
Pages (from-to)815-832
Number of pages18
JournalIntelligent Data Analysis
Issue number5
Publication statusPublished - 2009


  • Clustering of web documents
  • Semantic similarity
  • Single term similarity


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