Information Retrieval

, Volume 9, Issue 1, pp 33-53

First online:

Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments

  • Tuomo KoreniusAffiliated withDepartment of Computer Sciences, University of Tampere Email author 
  • , Jorma LaurikkalaAffiliated withDepartment of Computer Sciences, University of Tampere
  • , Martti JuholaAffiliated withDepartment of Computer Sciences, University of Tampere
  • , Kalervo JärvelinAffiliated withCenter for Advanced Studies, University of Tampere

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


Search facilitated with agglomerative hierarchical clustering methods was studied in a collection of Finnish newspaper articles (N = 53,893). To allow quick experiments, clustering was applied to a sample (N = 5,000) that was reduced with principal components analysis. The dendrograms were heuristically cut to find an optimal partition, whose clusters were compared with each of the 30 queries to retrieve the best-matching cluster. The four-level relevance assessment was collapsed into a binary one by (A) considering all the relevant and (B) only the highly relevant documents relevant, respectively. Single linkage (SL) was the worst method. It created many tiny clusters, and, consequently, searches enabled with it had high precision and low recall. The complete linkage (CL), average linkage (AL), and Ward's methods (WM) returned reasonably-sized clusters typically of 18–32 documents. Their recall (A: 27–52%, B: 50–82%) and precision (A: 83–90%, B: 18–21%) was higher than and comparable to those of the SL clusters, respectively. The AL and WM clustering had 1–8% better effectiveness than nearest neighbor searching (NN), and SL and CL were 1–9% less efficient that NN. However, the differences were statistically insignificant. When evaluated with the liberal assessment A, the results suggest that the AL and WM clustering offer better retrieval ability than NN. Assessment B renders the AL and WM clustering better than NN, when recall is considered more important than precision. The results imply that collections in the highly inflectional and agglutinative languages, such as Finnish, may be clustered as the collections in English, provided that documents are appropriately preprocessed.


Hierarchical clustering Graded relevance Finnish language Principal components analysis